Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US

Prediction of mammographic breast density based on clinical breast ultrasound images using deep learning: a retrospective analysis

Background

Breast density, as derived from mammographic images and defined by the Breast Imaging Reporting & Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound is an alternative breast cancer screening modality, particularly useful in low-resource, rural contexts. To date, breast ultrasound has not been used to inform risk models that need breast density. The purpose of this study is to explore the use of artificial intelligence (AI) to predict BI-RADS breast density category from clinical breast ultrasound imaging.

Methods

We compared deep learning methods for predicting breast density directly from breast ultrasound imaging, as well as machine learning models from breast ultrasound image gray-level histograms alone. The use of AI-derived breast ultrasound breast density as a breast cancer risk factor was compared to clinical BI-RADS breast density. Retrospective (2009-2022) breast ultrasound data were split by individual into 70/20/10% groups for training, validation, and held-out testing for reporting results.

Findings

405,120 clinical breast ultrasound images from 14,066 women (mean age 53 years, range 18-99 years) with clinical breast ultrasound exams were retrospectively selected for inclusion from three institutions: 10,393 training (302,574 images), 2593 validation (69,842), and 1074 testing (28,616). The AI model achieves AUROC 0.854 in breast density classification and statistically significantly outperforms all image statistic-based methods. In an existing clinical 5-year breast cancer risk model, breast ultrasound AI and clinical breast density predict 5-year breast cancer risk with 0.606 and 0.599 AUROC (DeLong's test p-value: 0.67), respectively.

Interpretation

BI-RADS breast density can be estimated from breast ultrasound imaging with high accuracy. The AI model provided superior estimates to other machine learning approaches. Furthermore, we demonstrate that age-adjusted, AI-derived breast ultrasound breast density provides similar predictive power to mammographic breast density in our population. Estimated breast density from ultrasound may be useful in performing breast cancer risk assessment in areas where mammography may not be available.

Funding

National Cancer Institute.

Sonographic Glandular Tissue Component: A Potential Imaging Marker for Upgrading BI-RADS 4A Breast Masses

PURPOSE

To investigate whether sonographic glandular tissue component (GTC) can optimize the management of breast imaging reporting and data system (BI-RADS) 4A breast masses.

MATERIALS AND METHODS

We reviewed the patients with BI-RADS 4A breast masses confirmed by ultrasound and pathology reports from January to December 2020. Based on conventional breast ultrasound images, GTC was categorized into GTC-Low and GTC-High. The consistency of the GTC classification between two radiologists was evaluated using a kappa test. Propensity score matching (PSM) was applied to adjust for unbalanced characteristics between the two groups. Logistic regression was used to analyze the relationship between sonographic GTC and the likelihood of BI-RADS 4A masses being benign or malignant.

RESULTS

Of the 319 patients included finally in the study, the agreement between the two radiologists regarding the GTC classification was good (weighted kappa: 0.736/0.716). The malignancy rate in the GTC-High group (32.7%, 16/49) was significantly higher than that in the overall cohort (14.1%, 45/319; P=0.001). After PSM adjustment to balance relevant covariates between the GTC-High and GTC-Low groups, 45 GTC-High patients were matched with 45 GTC-Low patients. After matching, univariate and multivariate logistic regression analyses identified sonographic GTC as an independent variable associated with malignancy in BI-RADS 4A masses (P=0.012). After matching, the malignancy rate in the GTC-High group (35.6%,16/45) was significantly higher (P=0.014) than that in the GTC-Low group (13.3%, 6/45).

CONCLUSION

Sonographic GTC is an independent predictor of malignancy in BI-RADS 4A breast masses. Masses initially classified as BI-RADS 4A may warrant reclassification to BI-RADS 4B when identified as GTC-High.

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional Networks (ResNet + FCN) segmentation model were trained. The better effective model was selected to appraise the classification performance of 3 breast radiologists and 3 non-breast radiologists. The evaluation metrics included sensitivity, specificity, and positive predictive value (PPV). The ResNet101 model demonstrated superior performance compared to the ResNet + FCN model. It significantly enhanced the classification sensitivity of all radiologists by 0.060, 0.021, 0.170, 0.009, 0.052, and 0.047, respectively. For P1 to P4 glandular, the PPVs of all radiologists increased by 0.154, 0.178, 0.027, and 0.109 with Ai-assisted. Notably, the non-breast radiologists experienced a particularly substantial rise in PPV (p < 0.01). This study trained ResNet 101 deep learning model is a reliable and accurate system for assisting different experienced radiologists differentiate dense breast glandular tissue components in ultrasound images.

Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection

Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. Utilizing 1,636 private mammograms, PMVnet combines cosine similarity and the squeeze-and-excitation method within a U-shaped architecture to leverage correlated information. Performance comparisons with single view-based models with VGGnet16, Resnet50, and EfficientnetB5 as encoders revealed PMVnet's superior capability. Using VGGnet16, PMVnet achieved a Dice Similarity Coefficient (DSC) of 0.709 in segmentation and a recall of 0.950 at 0.156 false positives per image (FPPI) in detection tasks, outperforming the single-view model, which had a DSC of 0.579 and a recall of 0.813 at 0.188 FPPI. These findings demonstrate PMVnet's effectiveness in reducing false positives and avoiding missed true positives, suggesting its potential as a practical tool in computer-aided diagnosis systems. PMVnet can significantly enhance breast lesion detection, aiding radiologists in making more precise evaluations and improving patient outcomes. Future applications of PMVnet may offer substantial benefits in clinical settings, improving patient care through enhanced diagnostic accuracy.

Altered mammary gland development and pro-tumorigenic changes in young female mice following prenatal BPAF exposure

Bisphenol A (BPA) is being phased out owing to its endocrine-disrupting effects and is increasingly being replaced by its substitute compounds such as bisphenol AF (BPAF). This study aims to explore the potential adverse outcomes of prenatal BPAF exposure combined with postnatal cross-fostering on the development and long-term health effects of the mammary gland in offspring. The results suggested that prenatal BPAF exposure accelerates the puberty, and induces duct dilatations, angiogenesis, lobular hyperplasia, and enhanced inflammatory cell infiltration in the mammary gland of female offspring. Differentially expressed genes exhibiting time series patterns induced by BPAF exposure were enriched in biological processes related to mammary gland development, epithelial cell proliferation and so on. Notably, 13 breast cancer-related biomarkers including Pgr, Gata3, Egfr and Areg were screened, showing a time-dependent increase in expression. After human homologous gene transformation, TCGA analysis suggested that the human homologues of genes differentially expressed in BPAF-treated mice were associated with increased tumor stages in female patients with breast cancer. Furthermore, postnatal cross-fostering did not completely restore the adverse effects of prenatal BPAF exposure and even showed a reverse tendency. These results imply that prenatal BPAF exposure in utero and postnatally nursing by BPAF exposed dams, have long-term effects on the mammary glands health of female offspring.

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).

Cabergoline as a Novel Strategy for Post-Pregnancy Breast Cancer Prevention in Mice and Human

Post-pregnancy breast cancer often carries a poor prognosis, posing a major clinical challenge. The increasing trend of later-life pregnancies exacerbates this risk, highlighting the need for effective chemoprevention strategies. Current options, limited to selective estrogen receptor modulators, aromatase inhibitors, or surgical procedures, offer limited efficacy and considerable side effects. Here, we report that cabergoline, a dopaminergic agonist, reduces the risk of breast cancer post-pregnancy in a Brca1/P53-deficient mouse model, with implications for human breast cancer prevention. We show that a single dose of cabergoline administered post-pregnancy significantly delayed the onset and reduced the incidence of breast cancer in Brca1/P53-deficient mice. Histological analysis revealed a notable acceleration in post-lactational involution over the short term, characterized by increased apoptosis and altered gene expression related to ion transport. Over the long term, histological changes in the mammary gland included a reduction in the ductal component, decreased epithelial proliferation, and a lower presence of recombinant Brca1/P53 target cells, which are precursors of tumors. These changes serve as indicators of reduced breast cancer susceptibility. Additionally, RNA sequencing identified gene expression alterations associated with decreased proliferation and mammary gland branching. Our findings highlight a mechanism wherein cabergoline enhances the protective effect of pregnancy against breast cancer by potentiating postlactational involution. Notably, a retrospective cohort study in women demonstrated a markedly lower incidence of post-pregnancy breast cancer in those treated with cabergoline compared to a control group. Our work underscores the importance of enhancing postlactational involution as a strategy for breast cancer prevention, and identifies cabergoline as a promising, low-risk option in breast cancer chemoprevention. This strategy has the potential to revolutionize breast cancer prevention approaches, particularly for women at increased risk due to genetic factors or delayed childbirth, and has wider implications beyond hereditary breast cancer cases.

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

BACKGROUND

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.

METHODS

We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.

RESULTS

We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.

CONCLUSIONS

The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

Beyond BI-RADS: Nonmass Abnormalities on Breast Ultrasound

Abnormalities on breast ultrasound (US) images which do not meet the criteria for masses are referred to as nonmass lesions. These features and outcomes have been investigated in several studies conducted by Asian researchers. However, the term "nonmass" is not included in the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) 5th edition for US. According to the Japan Association of Breast and Thyroid Sonology guidelines, breast lesions are divided into mass and nonmass. US findings of nonmass abnormalities are classified into five subtypes: abnormalities of the ducts, hypoechoic areas in the mammary glands, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. These findings can be benign or malignant; however, focal or segmental distributions and presence of calcifications suggest malignancy. Intraductal, invasive ductal, and lobular carcinomas can present as nonmass abnormalities. For the nonmass concept to be included in the next BI-RADS and be widely accepted in clinical practice, standardized terminologies, an interpretation algorithm, and outcome-based evidence are required for both screening and diagnostic US.

Nonmass Lesions on Breast US: An International Perspective on Clinical Use and Outcomes

Nonmass lesions (NMLs) on breast US are defined as discrete areas of altered echotexture compared to surrounding breast tissue and lack the 3-dimensionality of a mass. They are not a component of American College of Radiology BI-RADS, but they are a finding type included in the Japan Association of Breast and Thyroid Sonology lexicon. Use of the NML finding is routine in many Asian practices, including the Samsung Medical Center and Seoul National University Hospital, and their features and outcomes have been investigated in multiple studies. Nonmass lesions are most often observed when US is used to evaluate mammographic asymmetries, suspicious calcifications, and nonmass enhancement on MRI and contrast-enhanced mammography. Nonmass lesions can be described by their echogenicity, distribution, presence or absence of associated calcifications, abnormal duct changes, architectural distortion, posterior shadowing, small cysts, and hypervascularity. Malignant lesions, especially ductal carcinoma in situ, can manifest as NMLs on US. There is considerable overlap between the US features of benign and malignant NMLs, and they also must be distinguished from normal variants. The literature indicates that NMLs with linear or segmental distribution, associated calcifications, abnormal duct changes, posterior shadowing, and hypervascularity are suggestive of malignancy, whereas NMLs with only interspersed small cysts are usually benign fibrocystic changes. In this article, we introduce the concepts of NMLs, illustrate US features suggestive of benign and malignant etiologies, and discuss our institutional approach for evaluating NMLs and an algorithm that we use to guide interpretation in clinical practice.

Overcoming the limitations of screening mammography in Japan and Korea: a paradigm shift to personalized breast cancer screening based on ultrasonography

Screening mammography programs have been implemented in numerous Western countries with the aim of reducing breast cancer mortality. However, despite over 20 years of population-based screening mammography, the mortality rates in Japan and Korea continue to rise. This may be due to the fact that screening mammography is not as effective for Japanese and Korean women, who often have dense breasts. This density decreases the sensitivity of mammography due to a masking effect. Therefore, the early detection of small invasive cancers requires more than just mammography, particularly for women in their 40s. This review discusses the limitations and challenges of screening mammography, as well as the keys to successful population-based breast cancer screening in Japan and Korea. This includes a focus on breast ultrasonography techniques, which are based on histopathologic anatomical knowledge, and personalized screening strategies that are based on risk assessments measured by glandular tissue components.

Diagnostic value of shear wave elastography combined with super microvascular imaging for BI-RADS 3-5 nodules

Background

To investigate the diagnostic value of shear wave elastography (SWE) and super microvascular imaging (SMI) integrated with the traditional ultrasound breast imaging reporting and data system (BI-RADS) classification in differentiating between benign and malignant breast nodules.

Methods

For analysis, 88 patients with 110 breast nodules assessed as BI-RADS 3-5 by conventional ultrasound were selected. SWE and SMI evaluations were conducted separately, and all nodules were verified as benign or malignant ones by pathology. Receiver operating characteristic (ROC) curves were plotted after obtaining quantitative parameters of different shear waves of nodules, including maximum (Emax), mean (Emean), minimum (Emin) Young's modulus, modulus standard deviation (SD), and modulus ratio (Eratio). The best cut-off value, specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for diagnosing malignant nodules employing Emax were obtained, and the diagnostic value of combining Emax and BI-RADS classification was compared. SMI graded nodule based on the Alder blood flow grading standard, whereas the BI-RADS classification was based on microvascular morphology. We assessed the diagnostic value of SMI for breast nodules and investigated the diagnostic efficacy of SWE combined with SMI in differentiating benign and malignant breast nodules with BI-RADS classification 3-5.

Results

The adjusted the BI-RADS classification using SMI and SWE technologies promoted the sensitivity, specificity, and accuracy of discriminating benign and malignant breast nodules (P < 0.05). The combination of traditional ultrasound BI-RADS classification with SWE and SMI technologies offered high sensitivity, specificity, accuracy, PPV, and NPV for identifying benign and malignant breast lesions. Moreover, combining SWE and SMI technologies with the adjusted BI-RADS classificationhad the best diagnostic efficacy for distinguishing benign and malignant breast nodules with BI-RADS 3-5.

Conclusion

The combination of SWE and SMI with the adjusted BI-RADS classification is a promising diagnostic method for differentiating benign and malignant breast nodules.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI

Objectives

There is a clinical need for a non-ionizing, quantitative assessment of breast density, as one of the strongest independent risk factors for breast cancer. This study aims to establish proton density fat fraction (PDFF) as a quantitative biomarker for fat tissue concentration in breast MRI and correlate mean breast PDFF to mammography.

Methods

In this retrospective study, 193 women were routinely subjected to 3-T MRI using a six-echo chemical shift encoding-based water-fat sequence. Water-fat separation was based on a signal model accounting for a single T2* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T2*-values. After semi-automated breast segmentation, PDFF and T2* values were determined for the entire breast and fibroglandular tissue. The mammographic and MRI-based breast density was classified by visual estimation using the American College of Radiology Breast Imaging Reporting and Data System categories (ACR A-D).

Results

The PDFF negatively correlated with mammographic and MRI breast density measurements (Spearman rho: −0.74, p < .001) and revealed a significant distinction between all four ACR categories. Mean T2* of the fibroglandular tissue correlated with increasing ACR categories (Spearman rho: 0.34, p < .001). The PDFF of the fibroglandular tissue showed a correlation with age (Pearson rho: 0.56, p = .03).

Conclusion

The proposed breast PDFF as an automated tissue fat concentration measurement is comparable with mammographic breast density estimations. Therefore, it is a promising approach to an accurate, user-independent, and non-ionizing breast density assessment that could be easily incorporated into clinical routine breast MRI exams.

Key Points

• The proposed PDFF strongly negatively correlates with visually determined mammographic and MRI-based breast density estimations and therefore allows for an accurate, non-ionizing, and user-independent breast density measurement.

• In combination with T2*, the PDFF can be used to track structural alterations in the composition of breast tissue for an individualized risk assessment for breast cancer.