Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)

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.

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.

Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

Implications of breast density for breast cancer screening

ABSTRACT

Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors.

ESR Essentials: screening for breast cancer - general recommendations by EUSOBI

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. KEY POINTS: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. KEY RECOMMENDATIONS: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35-40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway

OBJECTIVE

To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.

MATERIALS AND METHOD

We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG.

RESULTS

We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4.

CONCLUSION

The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.

CLINICAL RELEVANCE STATEMENT

Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.

KEY POINTS

• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

[The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective]

BACKGROUND

Mammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to a better survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed.

OBJECTIVE

In recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP?

METHOD

The original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with a control group. The relevant current literature was also studied.

RESULTS

The distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies.

CONCLUSION

Retrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable a significant reduction in the workload of radiologists, fewer additional examinations and a reduced number of IBCs; however, the results of prospective studies are needed before implementation.

Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.

Association between breastfeeding, mammographic density, and breast cancer risk: a review

BACKGROUND

Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse.

MAIN FINDINGS

Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer.

CONCLUSION

This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.

Evaluation of apparent diffuse coefficient (ADC) with regards to reproducibility and diagnostic accuracy as well as possible significance of pre - and post - contrast acquisition and employment of different b values

PURPOSE

Ongoing efforts are focusing on optimizing diffusion-weighted imaging (DWI) as an essential part of breast MRI protocol. Our study aimed to evaluate the effect of contrast media (CM) on the apparent diffusion coefficients (ADC) acquired following current recommendations.

PATIENT AND METHODS

Patients who underwent 3 T breast MRI with a histologically verified suspicious lesion were included in this IRB-approved, single-center, cross-sectional retrospective study. Breast MRI protocol included a DWI sequence with multiple b-values, which was acquired before and after CM administration. ADC maps were calculated by in-line monoexponential fitting with b-values 0 /800 and 50/800. Two independent readers (R1, R2) reviewed the images in separate sessions for b values 0/800 and 50/800, pre- and post-CM. Bland Altmann plots as well as intraclass correlation coefficients (ICCs) for inter-reader agreement, different b-values, and pre- and post-CM were calculated. Diagnostic accuracy was evaluated and compared by calculating the area under the receiver operating characteristics curve (AUC).

RESULTS

91 lesions in 89 patients were examined (mean age 50.7 years, standard deviation 13.9). ADC values were significantly (P<0.05) lower post-CM (mean ranging from 1.28 x10-3 mm2/s to 1.30 x10-3 mm2/s) compared to pre-CM (mean ranging from 1.32 x10-3 mm2/s to 1.37 x10-3 mm2/s) for both b-values combinations (0/800 and 50/800 s/mm2). We found an almost perfect inter-reader agreement pre-/post-CM with b values 0/800 and 50/800 (ICC ranging from 0.853 to 0.939). Bland Altman plot demonstrated no systematic difference between readers. ROC analysis revealed good diagnostic performance without significant differences (P>0.05) between b values 0/800 and 50/800 s/mm2 as well as pre- and post-CM with areas under the ROC curve between 0.834 and 0.877.

CONCLUSION

ADC values are slightly lower when acquiring b values 0/800 and post-CM. This effect does not reduce the diagnostic performance but may be relevant in case of definite cut-offs in medical decision making.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI

BACKGROUND

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising.

PURPOSE

To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI.

STUDY TYPE

Retrospective.

POPULATION

133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2).

ASSESSMENT

DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale.

STATISTICAL TESTS

Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant.

RESULTS

Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error.

DATA CONCLUSION

Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

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 .

Potential of non-contrast spiral breast CT to exploit lesion density and favor breast cancer detection: A pilot study

PURPOSE

To assess the density values of breast lesions and breast tissue using non-contrast spiral breast CT (nc-SBCT) imaging.

METHOD

In this prospective study women undergoing nc-SBCT between April-October 2023 for any purpose were included in case of: histologically proven malignant lesion (ML); fibroadenoma (FA) with histologic confirmation or stability > 24 months (retrospectively); cysts with ultrasound correlation; and women with extremely dense breast (EDB) and no sonographic findings. Three regions of interest were placed on each lesion and 3 different area of EDB. The evaluation was performed by two readers (R1 and R2). Kruskal-Wallis test, intraclass correlation (ICC) and ROC analysis were used.

RESULTS

40 women with 12 ML, 10 FA, 15 cysts and 9 with EDB were included. Median density values and interquartile ranges for R1 and R2 were: 60.2 (53.3-67.3) and 62.5 (55.67-76.3) HU for ML; 46.3 (41.9-59.5) and 44.5 (40.5-59.8) HU for FA; 35.3 (24.3-46.0) and 39.7 (26.7-52.0) HU for cysts; and 28.7 (24.2-33.0) and 33.3 (31.7-36.8) HU for EDB. For both readers, densities were significantly different for ML versus EDB (p < 0.001) and cysts (p < 0.001) and for FA versus EDB (p=/<0.003). The AUC was 0.925 (95 %CI 0.858-0.993) for R1 and 0.942 (0.884-1.00) for R2 when comparing ML versus others and 0.792 (0.596-0.987) and 0.833 (0.659-1) when comparing ML versus FA. The ICC showed an almost perfect inter-reader (0.978) and intra-reader agreement (>0.879 for both readers).

CONCLUSIONS

In nc-SBCT malignant lesions have higher density values compared to normal tissue and measurements of density values are reproducible between different readers.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening

Assessing a woman's risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1-4) and a deep-learning texture risk model, with scores categorized into four quartiles (1-4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43-4.82)-4.57 (95% CI: 3.66-5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77-13.45)-16.94 (95% CI: 9.93-30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31-6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening.

Second breast cancer following negative breast MRI: Analysis by interval from surgery and risk factors

OBJECTIVES

This study aims to compare outcomes following a negative surveillance MRI study by surgery-MRI interval and investigate factors associated with second breast cancers in women with a personal history of breast cancer (PHBC).

METHODS

This retrospective study included 1552 consecutive women (mean age, 53 years) with a PHBC and a negative prevalence surveillance breast MRI result between August 2014 and December 2016. The incidence and characteristics of second breast cancers were reviewed and compared according to surgery-MRI interval (< 3 years vs ≥ 3 years). Logistic regression analysis was used to investigate associations with clinical-pathologic characteristics.

RESULTS

Twenty-five second breast cancers occurred after negative MRI. The incidence of second breast cancers or local-regional recurrence did not significantly differ by surgery-MRI interval. The median intervals between MRI to second breast cancer detection showed no significant difference between the two groups (surgery-MRI interval <3 years vs. ≥ 3 years). Two node-positive second breast cancers were detected in the group with <3 years interval. BRCA mutation status, receipt of breast-conserving surgery, and adjuvant chemotherapy (all p < .05) were significant factors associated with the development of second breast cancers.

CONCLUSION

Outcomes following a negative surveillance MRI did not differ by surgery-MRI interval. BRCA mutation status, receipt of breast-conserving surgery and adjuvant chemotherapy were independently associated with the risk of developing second breast cancers after negative surveillance MRI.

Variability in Breast Density Estimation and Its Impact on Breast Cancer Risk Assessment

Breast density is an independent risk factor for breast cancer, although variability exists in measurements. This study sought to evaluate the agreement between radiologists and automated breast density assessment software and assess the impact of breast density measures on breast cancer risk estimates using the Breast Cancer Surveillance Consortium (BCSC) model (v.2). A retrospective database search identified women who had undergone mammography between December 2021 and June 2022. The Breast Imaging Reporting and Data System (BI-RADS) breast composition index assigned by a radiologist (R) was recorded and analyzed using three commercially available software programs (S1, S2, and S3). The agreement rate and Cohen's kappa (κ) were used to evaluate inter-rater agreements concerning breast density measures. The 5-year risk of invasive breast cancer in women was calculated using the BCSC model (v.2) with breast density inputs from various density estimation methods. Absolute differences in risk between various density measurements were evaluated. Overall, 1,949 women (mean age, 53.2 years) were included. The inter-rater agreement between R, S1, and S2 was 75.0-75.6%, while that between S3 and the others was 60.2%-63.3%. Kappa was substantial between R, S1, and S2 (0.66-0.68), and moderate (0.49-0.50) between S3 and the others. S3 placed fewer women in mammographic density d (14.9%) than R, S1, and S2 (40.5%-44.0%). In BCSC risk assessment (v.2), S3 assessed fewer women with a high 5-year risk of invasive breast cancer than the other methods, resulting in an absolute difference of 0% between R, S1, and S2 in 75.0%-75.6% of cases, whereas the difference between S3 and the other methods occurs in 60.2%-63.3% of cases. Breast density assessment using various methods showed moderate-to-substantial agreement, potentially affecting risk assessments. Precise and consistent breast density measurements may lead to personalized and effective strategies for breast cancer prevention.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks?

Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.

Limited impact of adding digital breast tomosynthesis to full field digital mammography in an elevated breast cancer risk population

PURPOSE

To investigate the impact of adding digital breast tomosynthesis (DBT) to full field digital mammography (FFDM) in screening asymptomatic women with an elevated breast cancer life time risk (BCLTR) but without known genetic mutation.

METHODS

This IRB-approved single-institution multi-reader study on prospectively acquired FFDM + DBT images included 429 asymptomatic women (39-69y) with an elevated BC risk on their request form. The BCLTR was calculated for each patient using the IBISrisk calculator v8.0b. The screening protocol and reader study consisted of 4-view FFDM + DBT, which were read by four independent radiologists using the BI-RADS lexicon. Standard of care (SOC) included ultrasound (US) and magnetic resonance imaging (MRI) for women with > 30 % BCLTR. Breast cancer detection rate (BCDR), sensitivity and positive predictive value were assessed for FFDM and FFDM + DBT and detection outcomes were compared with McNemar-test.

RESULTS

In total 7/429 women in this clinically elevated breast cancer risk group were diagnosed with BC using SOC (BCDR 16.3/1000) of which 4 were detected with FFDM. Supplemental DBT did not detect additional cancers and BCDR was the same for FFDM vs FFDM + DBT (9.3/1000, McNemar p = 1). Moderate inter-reader agreement for diagnostic BI-RADS score was found for both study arms (ICC for FFDM and FFDM + DBT was 0.43, resp. 0.46).

CONCLUSION

In this single institution study, supplemental screening with DBT in addition to standard FFDM did not increase BCDR in this higher-than-average BC risk group, objectively documented using the IBISrisk calculator.

Current use and future perspectives of contrast-enhanced mammography (CEM): a survey by the European Society of Breast Imaging (EUSOBI)

OBJECTIVES

To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM).

METHODS

A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics.

RESULTS

Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001).

CONCLUSIONS

Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique.

CLINICAL RELEVANCE STATEMENT

In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications.

KEY POINTS

• Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience.

Accelerating breast MRI acquisition with generative AI models

OBJECTIVES

To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction.

MATERIALS AND METHODS

We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations.

RESULTS

The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20.

CONCLUSION

Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds.

CLINICAL RELEVANCE STATEMENT

The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible.

KEY POINTS

Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Awareness of Breast Cancer Risk Factors in Women with vs. Without High Breast Density

Purpose

Women with high breast density (HBD) carry an increased risk for breast cancer (BC). The aim of the study was to provide data on awareness and knowledge gaps among women with vs w/o HBD about BC risk factors (BCRFs), which is the basis for effective communication about screening.

Patients and Methods

This was a web-based survey of 3000 women aged ≥30 and ≤70 from six countries. It comprised of 45 questions. T-tests and chi-square tests with False Discovery Rate adjustments were conducted as applicable, with significant differences reported at α=0.05.

Results

Three-thousand women were included in the analysis, 733 (24.4%) had HBD. Overall, 39% of women were familiar with the concept of HBD in the context of BC. Thirty-one percent of women were aware of HBD as BCRF and for 24% of women HBD was personally applicable. A significantly higher proportion of women with HBD were aware of almost all BCRFs compared to women w/o HBD (p ≤ 0.05). Similarly, a significantly higher proportion of women with HBD have undergone screening procedures compared to women w/o HBD (p ≤ 0.05). Women with HBD were significantly better aware of basic facts about BC (p ≤ 0.05). A total of 1617 women underwent mammography, 904 ultrasound and 150 MRI during their last screening. The most relevant source of information about BC was the health care professional, as reported by 63% of women.

Conclusion

Overall 39% of women were familiar with HBD as BCRF. Lack of BCRF awareness may contribute to delayed screenings, missed opportunities for early detection, and potentially poorer outcomes for individuals with dense breast tissue. Thus, this information should be communicated more widely.

PROMRIINE (PRe-operatory Magnetic Resonance Imaging is INEffective) Study: A Systematic Review and Meta-analysis of the Impact of Magnetic Resonance Imaging on Surgical Decisions and Clinical Outcomes in Women with Breast Cancer

BACKGROUND

The purpose of this study was to review and summarize the association between preoperative magnetic resonance imaging (MRI) and surgical outcomes in women with newly diagnosed invasive breast cancer from published randomized controlled trials (RCT).

MATERIALS AND METHODS

Two independent researchers conducted a systematic review through a comprehensive search of electronic databases, including PubMed, Medline, Embase, Ovid, Cochrane Library, and Web of Science. If there was disagreement between the two reviewers, a third reviewer assessed the manuscript to determine whether it should be included for data extraction. The quality of the papers was assessed using the risk of bias tool, and the evidence was analyzed using GRADE. Meta-analyses using a fixed-effects model were used to estimate the pooled risk ratio (RR) and 95% confidence interval (CI).

RESULTS

Initially, 21 studies were identified, 15 of which were observational comparative studies. A total of five RCTs were included, and they suggested that preoperative MRI significantly reduced the rate of immediate breast-conserving surgery and increased the risk for mastectomy.

CONCLUSIONS

From the RCT perspective, preoperative MRI for newly diagnosed invasive breast cancer did not improve surgical outcomes and may increase the risk of mastectomy.

Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging

BACKGROUND

There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI.

PURPOSE

To develop a generative model for gadolinium-free contrast-enhanced breast MRI and evaluate the diagnostic utility of the generated scans.

STUDY TYPE

Retrospective.

POPULATION

Two hundred seventy-six women with 304 breast MRI examinations (49 ± 13 years, 243/61 for training/testing).

FIELD STRENGTH/SEQUENCE

ZOOMit diffusion-weighted imaging (DWI), T1-weighted volumetric interpolated breath-hold examination (T1W VIBE), and axial T2 3D SPACE at 3.0 T.

ASSESSMENT

A generative model was developed to generate contrast-enhanced scans using precontrast T1W VIBE and DWI images. The generated and real images were quantitatively compared using the structural similarity index (SSIM), mean absolute error (MAE), and Dice similarity coefficient. Three radiologists with 8, 5, and 5 years of experience independently rated the image quality and lesion visibility on AI-generated and real images within various subgroups using a five-point scale. Four breast radiologists, with 8, 8, 5, and 5 years of experience, independently and blindly interpreted four reading protocols: unenhanced MRI protocol alone and combined with AI-generated scans, abbreviated MRI protocol, and full-MRI protocol.

STATISTICAL ANALYSIS

Results were assessed using t-tests and McNemar tests. Using pathology diagnosis as reference standard, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each reading protocol. A P value <0.05 was considered significant.

RESULTS

In the test set, the generated images showed similarity to the real images (SSIM: 0.935 ± 0.047 [SD], MAE: 0.015 ± 0.012 [SD], and Dice coefficient: 0.726 ± 0.177 [SD]). No significant difference in lesion visibility was observed between real and AI-generated scans of the mass, non-mass, and benign lesion subgroups. Adding AI-generated scans to the unenhanced MRI protocol slightly improved breast cancer detection (sensitivity: 92.86% vs. 85.71%, NPV: 76.92% vs. 70.00%); achieved non-inferior diagnostic utility compared to the AB-MRI protocol and full-protocol (sensitivity: 92.86%, 95.24%; NPV: 75.00%, 81.82%).

DATA CONCLUSION

AI-generated gadolinium-free contrast-enhanced breast MRI has potential to improve the sensitivity of unenhanced MRI in detecting breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 3.

Validation of Contrast-Enhanced Mammography as Breast Imaging Modality Compared to Standard Mammography and Digital Breast Tomosynthesis

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings.

Breast density effect on the sensitivity of digital screening mammography in a UK cohort

OBJECTIVES

To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort.

METHODS

Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold.

RESULTS

Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5.

CONCLUSIONS

The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations.

CLINICAL RELEVANCE STATEMENT

In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category.

KEY POINTS

Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.

The verification of the utility of a commercially available phantom combination for quality control in contrast-enhanced mammography

Contrast-enhanced mammography is being increasingly implemented clinically, providing much improved contrast between tumour and background structures, particularly in dense breasts. Although CEM is similar to conventional mammography it differs via an additional exposure with high energy X-rays (≥ 40 kVp) and subsequent image subtraction. Because of its special operational aspects, the CEM aspect of a CEM unit needs to be uniquely characterised and evaluated. This study aims to verify the utility of a commercially available phantom set (BR3D model 020 and CESM model 022 phantoms (CIRS, Norfolk, Virginia, USA)) in performing key CEM performance tests (linearity of system response with iodine concentration and background subtraction) on two models of CEM units in a clinical setting. The tests were successfully performed, yielding results similar to previously published studies. Further, similarities and differences in the two systems from different vendors were highlighted, knowledge of which may potentially facilitate optimisation of the systems.

Auditing Abbreviated Breast MR Imaging: Clinical Considerations and Implications

Abbreviated breast MR (AB-MR) imaging is a relatively new breast imaging tool, which maintains diagnostic accuracy while reducing image times compared with full-protocol breast MR (FP-MR) imaging. Breast imaging audits involve calculating individual and organizational metrics, which can be compared with established benchmarks, providing a standard against which performance can be measured. Unlike FP-MR imaging, there are no established benchmarks for AB-MR imaging but studies demonstrate comparable performance for cancer detection rate, positive predictive value 3, sensitivity, and specificity with T2. We review the basics of performing an audit, including strategies to implement if benchmarks are not being met.

Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network

OBJECTIVES

To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions.

METHODS

Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed.

RESULTS

1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%.

CONCLUSION

DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

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).

Cancer detection rate of breast-MR in supplemental screening after negative mammography in women with dense breasts. Preliminary results of the MA-DETECT-Study after 200 participants

BACKGROUND

Due to increased cancer detection rates (CDR), breast MR (breast MRI) can reduce underdiagnosis of breast cancer compared to conventional imaging techniques, particularly in women with dense breasts. The purpose of this study is to report the additional breast cancer yield by breast MRI in women with dense breasts after receiving a negative screening mammogram.

METHODS

For this study we invited consecutive participants of the national German breast cancer Screening program with breast density categories ACR C & D and a negative mammogram to undergo additional screening by breast MRI. Endpoints were CDR and recall rates. This study reports interim results in the first 200 patients. At a power of 80% and considering an alpha error of 5%, this preliminary population size is sufficient to demonstrate a 4/1000 improvement in CDR.

RESULTS

In 200 screening participants, 8 women (40/1000, 17.4-77.3/1000) were recalled due to positive breast MRI findings. Image-guided biopsy revealed 5 cancers in 4 patients (one bilateral), comprising four invasive cancers and one case of DCIS. 3 patients revealed 4 invasive cancers presenting with ACR C breast density and one patient non-calcifying DCIS in a woman with ACR D breast density, resulting in a CDR of 20/1000 (95%-CI 5.5-50.4/1000) and a PPV of 50% (95%-CI 15.7-84.3%).

CONCLUSION

Our initial results demonstrate that supplemental screening using breast MRI in women with heterogeneously dense and very dense breasts yields an additional cancer detection rate in line with a prior randomized trial on breast MRI screening of women with extremely dense breasts. These findings are highly important as the population investigated constitutes a much higher proportion of women and yielded cancers particularly in women with heterogeneously dense breasts.

Enhancing the Kaiser score for lesion characterization in unenhanced breast MRI

PURPOSE

To adapt the methodology of the Kaiser score, a clinical decision rule for lesion characterization in breast MRI, for unenhanced protocols.

METHOD

In this retrospective IRB-approved cross-sectional study, we included 93 consecutive patients who underwent breast MRI between 2021 and 2023 for further work-up of BI-RADS 0, 3-5 in conventional imaging or for staging purposes (BI-RADS 6). All patients underwent biopsy for histologic verification or were followed for a minimum of 12 months. MRI scans were conducted using 1.5 T or 3 T scanners using dedicated breast coils and a protocol in line with international recommendations including DWI and ADC. Lesion characterization relied solely on T2w and DWI/ADC-derived features (such as lesion type, margins, shape, internal signal, surrounding tissue findings, ADC value). Statistical analysis was done using decision tree analysis aiming to distinguish benign (histology/follow-up) from malignant outcomes.

RESULTS

We analyzed a total of 161 lesions (81 of them non-mass) with a malignancy rate of 40%. Lesion margins (spiculated, irregular, or circumscribed) were identified as the most important criterion within the decision tree, followed by the ADC value as second most important criterion. The resulting score demonstrated a strong diagnostic performance with an AUC of 0.840, providing both rule-in and rule-out criteria. In an independent test set of 65 lesions the diagnostic performance was verified by two readers (AUC 0.77 and 0.87, kappa: 0.62).

CONCLUSIONS

We developed a clinical decision rule for unenhanced breast MRI including lesion margins and ADC value as the most important criteria, achieving high diagnostic accuracy.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening

OBJECTIVES

To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density.

METHODS

Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D).

RESULTS

Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively).

CONCLUSIONS

Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts.

CLINICAL RELEVANCE STATEMENT

Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts.

KEY POINTS

• Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Optimized, Person-Centered Workflow Design for a High-Throughput Breast MRI Screening Facility-A Simulation Study

OBJECTIVES

This project aims to model an optimal scanning environment for breast magnetic resonance imaging (MRI) screening based on real-life data to identify to what extent the logistics of breast MRI can be optimized.

MATERIALS AND METHODS

A novel concept for a breast MRI screening facility was developed considering layout of the building, workflow steps, used resources, and MRI protocols. The envisioned screening facility is person centered and aims for an efficient workflow-oriented design. Real-life data, collected from existing breast MRI screening workflows, during 62 scans in 3 different hospitals, were imported into a 3D simulation software for designing and testing new concepts. The model provided several realistic, virtual, logistical pathways for MRI screening and their outcome measures: throughput, waiting times, and other relevant variables.

RESULTS

The total average appointment time in the baseline scenario was 25:54 minutes, with 19:06 minutes of MRI room occupation. Simulated improvements consisted of optimizing processes and resources, facility layout, and scanning protocol. In the simulation, time spent in the MRI room was reduced by introducing an optimized facility layout, dockable tables, and adoption of an abbreviated MRI scanning protocol. The total average appointment time was reduced to 19:36 minutes, and in this scenario, the MRI room was occupied for 06:21 minutes. In the most promising scenario, screening of about 68 people per day (10 hours) on a single MRI scanner could be feasible, compared with 36 people per day in the baseline scenario.

CONCLUSIONS

This study suggests that by optimizing workflow MRI for breast screening total appointment duration and MRI occupation can be reduced. A throughput of up to 6 people per hour may be achieved, compared with 3 people per hour in the current setup.

Evaluating the impact of delayed-phase imaging in Contrast-Enhanced Mammography on breast cancer staging: A comparative study of abbreviated versus complete protocol

PURPOSE

Contrast-enhanced mammography (CEM) is an innovative imaging tool for breast cancer detection, involving intravenous injection of a contrast medium and the assessment of lesion enhancement in two phases: early and delayed. The aim of the study was to analyze the topographic concordance of lesions detected in the early- versus delayed phase acquisitions.

MATERIALS AND METHODS

Approved by the Ethics Committee (No. 118/20), this prospective study included 100 women with histopathological confirmed breast neoplasia (B6) at the Radiodiagnostics Department of the Maggiore della Carità Hospital of Novara, Italy from May 1, 2021, to October 17, 2022. Participants underwent CEM examinations using a complete protocol, encompassing both early- and delayed image acquisitions. Three experienced radiologists blindly analyzed the CEM images for contrast enhancement to determine the topographic concordance of the identified lesions. Two readers assessed the complete study (protocol A), while one reader assessed the protocol without the delayed phase (protocol B). The average glandular dose (AGD) of the entire procedure was also evaluated.

RESULTS

The analysis demonstrated high concordance among the three readers in the topographical identification of lesions within individual quadrants of both breasts, with a Cohen's κ > 0.75, except for the lower inner quadrant of the right breast and the retro-areolar region of the left breast. The mean whole AGD was 29.2 mGy. The mean AGD due to CEM amounted to 73% of the whole AGD (21.2 mGy). The AGD attributable to the delayed phase of CEM contributed to 36% of the whole AGD (10.5 mGy).

CONCLUSIONS

As we found no significant discrepancy between the readings of the two protocols, we conclude that delayed-phase image acquisition in CEM does not provide essential diagnostic benefits for effective disease management. Instead, it contributes to unnecessary radiation exposure.

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.

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.

Role of Supplemental Breast MRI in Screening Women with Mammographically Dense Breasts: A Systematic Review and Meta-analysis

BACKGROUND

High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk.

METHODS

PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277).

RESULTS

Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies.

CONCLUSION

Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings.

ACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update

Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

[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.

Stereotactic biopsy with contrast-enhanced mammography: the initial Australian experience

INTRODUCTION

Contrast-enhanced mammography (CEM) and MRI detect 'contrast-only' lesions (COLs) occult on standard breast imaging (ultrasound and conventional mammography). Until recently, MRI was the only reliable method of biopsy. This study presents the first Australian experience with CEM-guided biopsy (CEMBx) and the lessons learnt.

METHODS

A prospective audit of the first 15 consecutive patients who underwent CEMBx for COLs was performed. Indications for contrast imaging, patient and lesion characteristics, procedural details, radiation dose and pathology data were collected.

RESULTS

The 15 women were aged 37-81 years (mean 59 years). Indications for contrast imaging were problem solving (n = 3), moderate risk screening (n = 2), cancer staging (n = 9) and symptoms (n = 1). The COLs were non-mass (n = 14), mass (n = 1) and an enhancing asymmetry (n = 1). For one patient, two lesions were sampled during the same event. All lesions enhanced and were successfully sampled followed by marker clip insertion. Most biopsies (87.5%) were performed with the breast in cranio-caudal compression using a horizontal approach. Procedural duration ranged from 13 to 33 min (mean 22 min). Radiation dose was similar to standard stereotactic biopsy. Post-biopsy hematomas occurred in three patients, none required intervention. Clip displacement occurred in three cases. Core biopsy histopathology results were benign (n = 8), malignant (n = 7) and a borderline breast lesion (BBL) (n = 1). Patient satisfaction rates were high. Imaging follow-up is ongoing.

CONCLUSIONS

CEMBx is a quick, safe and reliable alternative to MRIBx to sample COLs.

Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network's predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

Forget Me Not: Incidental Findings on Breast MRI

With the growing utilization and expanding role of breast MRI, breast imaging radiologists may encounter an increasing number of incidental findings beyond the breast and axilla. Breast MRI encompasses a large area of anatomic coverage extending from the lower neck to the upper abdomen. While most incidental findings on breast MRI are benign, identifying metastatic disease can have a substantial impact on staging, prognosis, and treatment. Breast imaging radiologists should be familiar with common sites, MRI features, and breast cancer subtypes associated with metastatic disease to assist in differentiating malignant from benign findings. Furthermore, detection of malignancies of nonbreast origin as well as nonmalignant, but clinically relevant, incidental findings can significantly impact clinical management and patient outcomes. Breast imaging radiologists should consistently follow a comprehensive search pattern and employ techniques to improve the detection of these important incidental findings.

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.