Fibroglandular Tissue Research Articles

Increased background parenchymal enhancement on peri-menopausal breast magnetic resonance imaging

ObjectivesTo examine the background parenchymal enhancement (BPE) levels in peri-menopausal breast MRI compared with pre- and post-menopausal breast MRI. MethodsThis study included 562 patients (55.8±12.3 years) who underwent contrast-enhanced dynamic breast MRI between 2011 and 2015 for clinical indications. We evaluated the BPE level, amount of fibroglandular tissue (FGT), and social and clinical variables. The inter-reader agreement for the amount of FGT and the BPE level was evaluated using interclass correlation coefficients. Associations between the BPE level and body mass index (BMI), ages of menarche and menopause, childbirth history, number of children, and the amount of FGT were determined using Spearman’s correlation coefficients or Mann-Whitney U-test. Pearson’s χ2 test was used to assess the difference in the frequency of BPE categories among the age-groups. ResultsThe inter-reader agreement was 0.864 for the amount of FGT and 0.840 for the BPE level, both indicating almost perfect agreement. The BPE level showed a weak positive correlation with the amount of FGT (Spearman’s ρ=0.271, P<0.001). BPE was not significantly correlated with BMI, childbirth history, number of births, or ages of menarche or menopause. BPE was greater in the peri-menopausal age-group compared with the corresponding pre- and post-menopausal age-groups, both with benign and malignant lesions. ConclusionsBPE was greater in the peri-menopausal stage than in the pre- and post-menopausal stages. Our results suggest that BPE showed a non-linear decrease with age and that the hormonal disbalance in the peri-menopausal period has a greater effect on the BPE level than was previously assumed.

Clinical and Imaging features associated with malignant focal non-mass enhancement on breast MRI

Introduction: Focal non-mass enhancement (NME) is a common breast MRI finding with limited data to guide management. This study aimed to assess clinical and imaging features of malignant BI-RADS 4 focal NME. MethodsThis IRB-approved, retrospective study included breast MRI exams between August 1, 2013 and September 1, 2022 yielding BI-RADS 4 focal NME lesions that underwent core biopsy or excision with available pathology result or demonstrated decrease or resolution during follow-up MRI or at least two years of MRI stability. ResultsA total of 296 BI-RADS 4 focal NME lesions in 246 patients were included in the study. The overall malignancy rate of BI-RADS 4 focal NME was 36/296 (12.2%). Focal NME in a patient presenting for evaluation of extent of disease or other diagnostic concern was 5.5 and 3.4 times more likely, respectively, to be malignant compared to focal NME seen on a high-risk screening exam. There was also a significant association between malignancy and focal NME that was brighter than background parenchymal enhancement (BPE) on maximum intensity projection (MIP) images. There was no significant association between malignancy and lesion size, internal enhancement pattern, amount of BPE, amount of fibroglandular tissue, or signal intensity on T2-weighted images. ConclusionOur study yielded a malignancy rate of 12.2% for BI-RADS 4 focal NME lesions. Indication for MRI and signal intensity compared to BPE on MIP images were features associated with malignancy that may provide guidance on the management for focal NME. Micro abstractFocal non-mass enhancement (NME) is a common breast MRI finding with limited data to guide management. In this rertospective study of 296 BI-RADS 4 focal NME lesions in 246 patients, 36/296 (12.2%) were malignant. Lesions were more likely to be malignant if they were brighter than background parenchymal enhancement or if the exam was a diagnostic, rather than screening, exam.

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learning-based prediction model via an attention mechanism. Our topology-informed deep learning model, TopoTxR, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate TopoTxR using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures. We further demonstrate TopoTxR’s efficacy in predicting response to neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-naïve imaging, in patients who respond favorably to therapy as achieving pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines on the publicly available I-SPY 1 dataset (N = 161, including 47 patients with pCR and 114 without) and the Rutgers proprietary dataset (N = 120, with 69 patients achieving pCR and 51 not), TopoTxR demonstrates a notable improvement, achieving a 2.6% increase in accuracy and a 4.6% enhancement in AUC compared to the state-of-the-art method.

Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients

Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast’s fibroglandular tissue (FGT) in breast cancer patients. Materials and methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26–82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-naïve breast cancer. Patients were divided into training (n = 250) and validation (n = 291) sets. In the training set, 132 radiomic features were extracted using the opensource CERR platform. Following feature selection, the final prediction model was created, based on a support vector machine with a polynomial kernel of order 2. Results: In the validation set, the final prediction model, which included four radiomic features, achieved an F1 score of 0.66, an area under the curve of 0.71, a sensitivity of 54% [47–60%], a specificity of 74% [65–84%], a positive predictive value of 84% [78–90%], and a negative predictive value of 39% [31–47%]. Conclusions: TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.

Understanding the risk of ionizing radiation in breast imaging: Concepts and quantities, clinical importance, and future directions

BackgroundConventional mammography remains the primary imaging modality for state-of-the-art breast imaging practice and its benefit (both on diagnostic and screening) was largely reported. In mammography, the typical Mean Glandular Dose (MGD) from X-ray radiation to the breast spans, on average, from 1 to 10 mGy, depending on breast thicknesses, percentage of fibroglandular tissue, and on the examination purpose. MethodsThe aim of this narrative review is to describe the extent of radiation risk in X-ray breast imaging and discuss the main steps and parameters (e.g. MGD, screening frequency and number of examination views) that may have an influence on the radiation risk assessment. ResultsEven though the radiation doses used with these examinations are very low, as compared to other medical or natural radiation exposures, there is a non-negligible associated risk of radiation-induced cancer. Accurate radiation risk assessment permits to better balance the overall estimation of the benefit-to-risk ratio in X-ray breast imaging. ConclusionsIt is expected that a better knowledge about radiation-induced cancer risk among population could improve the communications skills between patients and clinicians and could help to increase the awareness in women about radiation risk perception for a transparent and proper informed choice of imaging exam.

Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study

Background/Objectives: High breast density is a risk factor for breast cancer and can reduce the sensitivity of mammography. Given the influence of breast density on patient risk stratification and screening accuracy, it is crucial to monitor the prevalence of extremely dense breasts within local populations. Moreover, there is a lack of comprehensive understanding regarding breast density prevalence in Switzerland. Therefore, this study aimed to determine the prevalence of breast density in a selected Swiss population. Methods: To overcome the potential variability in breast density classifications by human readers, this study utilized commercially available deep convolutional neural network breast classification software. A retrospective analysis of mammographic images of women aged 40 years and older was performed. Results: A total of 4698 mammograms from women (58 ± 11 years) were included in this study. The highest prevalence of breast density was in category C (heterogeneously dense), which was observed in 41.5% of the cases. This was followed by category B (scattered areas of fibroglandular tissue), which accounted for 22.5%. Conclusion: Notably, extremely dense breasts (category D) were significantly more common in younger women, with a prevalence of 34%. However, this rate dropped sharply to less than 10% in women over 55 years of age.

The Incidence of Breast Cancer in Egyptian Females in Correlation to Different Mammographic ACR Densities

Abstract Background Breast density, radiologically, referred as fibro glandular mammary tissue, was found to be a predisposing factor for breast cancer (BC). However, the stated degree of elevated BC risk varies widely in the literature. Objective To detect the relationship between different breast mammographic densities and risk for BC in Egyptian females. Patients and Methods An analytical cross-sectional prospective study was conducted at Ain Shams University Hospital, National Project for Women Health and Private Centre in the period from December 2020 to December 2021. The study was applied to 814 asymptomatic females above 35 years old who came for BC screening using full-field digital mammography. Results The incidence of BC was found in 84 females (10.3%). The BC incidence in females with ACR-A was 6.3%, ACR-B was 8.5%, ACR-C was 16.3% and ACR-D was 27.8%, with a p- value (p &amp;lt; 0.001). Using logistic regression analysis showed that three types of breast densities increased the risk of BC, where patients with type-B, type-C, and type-D breast density had a 1.39, 2.92, and 3.12 times more risk for BC, respectively (p value= 0.010, 0.003, and 0.036 respectively). Conclusion Among Egyptian females, the incidence of BC is 10.3%. Our data revealed that the more breast density, the higher the incidence of BC, and affirmed that breast density is a risk factor for BC development.

Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening.

In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant challenge in diagnostic imaging. The current study focuses on the automatic segmentation of breast glandular tissue in MRI scans using a deep learning model. The aim is to establish a solid foundation for the development of methods for the precise quantification of fibroglandular tissue. For this purpose, the publicly available 'Duke Breast Cancer MRI' dataset was systematically processed to train a deep neural network model utilizing the nnU-Net ('no-new-Net') framework, which was then subjected to a quantitative evaluation. The results show the following macro-averaged metrics with standard deviation: Dice Similarity Coefficient 0.827 ± 0.152, accuracy 0.997 ± 0.003, sensitivity 0.825 ± 0.158, and specificity 0.999 ± 0.001. The effectiveness of our model in segmenting FGT is underscored by the high values of the Dice coefficient, Accuracy, Sensitivity, and Specificity, which reflect the precision and reliability of our results. The findings of this study lay a solid foundation for developing automated methods to quantify FGT. Our research efforts, especially driven by clinical studies at the University Hospital Augsburg, are focused on further exploring and validating these potentials.

In vivo optimization of the experimental conditions for the non-invasive optical assessment of breast density

In this study, time domain diffuse optical spectroscopy is performed in the range 600–1100 nm on 11 healthy volunteers with a portable system for the quantitative characterization of breast tissue in terms of optical properties and optically-derived blood parameters, tissue constituent concentrations, and scattering parameters. A measurement protocol involving different geometries (reflectance and transmittance), subject’s positions (sitting and lying down), probing locations (outer, lower, and inner breast quadrants), and source-detector distances (2 and 3 cm) allowed us to investigate the effect of tissue heterogeneity and different measurement configurations on the results with the aim of identifying the best experimental conditions for the estimate of breast density (i.e., amount of fibro-glandular tissue in the breast) as a strong independent risk factor for breast cancer. Transmittance results, that in previous studies correlated strongly with mammographic density, are used as a reference for the initial test of the simpler and more comfortable reflectance measurement configuration. The higher source-detector distance, which probes deeper tissue, retrieves optical outcomes in agreement with higher average density tissue. Similarly, results on the outer quadrants indicate higher density than internal quadrants. These findings are coherent with breast anatomy since the concentration of dense fibro-glandular stroma is higher in deep tissue and towards the external portion of the breast, where the mammary gland is located. The dataset generated with this laboratory campaign is used to device an optimal measurement protocol for a future clinical trial, where optical results will be correlated with conventional mammographic density, allowing us to identify a subset of wavelengths and measurement configurations for an effective estimate of breast density. The final objective is the design of a simplified, compact and cost-effective optical device for a non-invasive, routine assessment of density-associated breast cancer risk.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography

BackgroundPhoton counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. MethodsTo address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. ResultsTo verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. ConclusionExperimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Risk factors for residual fibroglandular breast tissue following a mastectomy - an overview and retrospective cohort study

BackgroundResidual fibroglandular breast tissue (RFGT) following a mastectomy is associated with the remaining of occult breast cancer at the time of mastectomy as well as an increased local recurrence risk thereafter. Despite its oncologic implications, data on measures to prevent RFGT are lacking. Therefore, in a first step knowledge of risk factors for RFGT is of uttermost importance in order to allow identification of patients at risk and subsequently adaption of the surgical treatment and potentially prevention of RFGT a priori.MethodsWe performed a systematic literature review in PubMed using the MESH terms [residual fibroglandular breast tissue], [residual breast tissue], [mastectomy] and [risk factor] followed by a retrospective data analysis including all patients with a mastectomy treated at the Department of Obstetrics and Gynecology of the Medical University of Vienna, Austria, between 01.01.2015 and 26.02.2020 in order to identify risk factors of RFGT following a mastectomy. The primary aim of the study was to assess a potential difference in RFGT volume between the different types of mastectomy. The secondary objectives of the study were to identify other potential risk factors for RFGT as well as to compare the skin and subcutaneous fat tissue thickness pre- to postoperatively.ResultsSignificantly higher RFGT volumes were observed following a nipple-sparing mastectomy (NSM) compared to a skin-sparing mastectomy (SSM) and radical mastectomy (RME) (p < .001). Furthermore, RFGT volume was significantly associated with the variables: reconstruction (p = .012), acellular dermal matrix (ADM) or mesh (p = .031), patient age (p = .022), preoperative fibroglandular tissue (FGT) volume (p = .012) and preoperative whole breast volume (including the skin envelope and nipple-areola-complex) (p = .030). The reduction in the postoperative compared to preoperative skin envelope thickness measured medially and laterally reached statistical significance in the NSM-cohort (medial p < .001, lateral p = .001) and showed a numerical difference in the RME and SSM-cohort.ConclusionMastectomy type, reconstruction, ADM or mesh, patient age, preoperative FGT volume and whole breast volume were identified as risk factors for RFGT in univariable analysis. The observed reduction in the post- compared to preoperative skin envelope thickness should be avoided considering the known associated increase in risk for ischemic complications.

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

BackgroundHigher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD.MethodsIn this cohort of 611 invasive breast cancer patients (ages 23–91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype.ResultsWe found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08–1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11–1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09–1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors.ConclusionsOur findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

Value of digital breast tomosynthesis in characterization of breast lesions in dense breast

BackgroundPatients with dense breasts have a higher risk factor for developing breast cancer. Digital mammography (DM) exhibits a reduced sensitivity in dense breast tissue as a result of overlapping fibro-glandular tissue. Digital breast tomosynthesis (DBT) solves this problem, so allows better characterization of masses, asymmetries, and parenchymal distortion. It is useful in screening studies for early detection of breast lesions by accurate detection and description of their margins, shape, and extent as well as surrounding structures.ObjectiveWe aimed to evaluate the added value of DBT in characterizing of different breast lesions in dense breasts compared to DM.MethodsA Prospective Cohort study involved ninety female cases having dense breast parenchyma (ACR C & ACR D) having breast complaint lesions detected by DM or ultrasound or recalled from screening units, during the period from June 2021 to Jan 2023.All patient underwent DM and DBT with correlation with the pathological finding or follow up.ResultsAs regards the characterization of breast mass DBT had higher sensitivity, higher specificity (98%, 87% respectively). However, DM showed less sensitivity, less specificity (58%, 80%,) with (p < 0.0001, p = 0.0005 respectively).ConclusionsDBT effectively visualizes overlapping tissue in dense breasts. DBT is important due to its ability to detect breast cancer earlier, characterize lesions accurately, and inspect the margins of masses precisely, along with decreasing recall rates for false-positive results.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

23Na MRI: inter-reader reproducibility of normal fibroglandular sodium concentration measurements at 3 T

BackgroundTo study the reproducibility of 23Na magnetic resonance imaging (MRI) measurements from breast tissue in healthy volunteers.MethodsUsing a dual-tuned bilateral 23Na/1H breast coil at 3-T MRI, high-resolution 23Na MRI three-dimensional cones sequences were used to quantify total sodium concentration (TSC) and fluid-attenuated sodium concentration (FASC). B1-corrected TSC and FASC maps were created. Two readers manually measured mean, minimum and maximum TSC and mean FASC values using two sampling methods: large regions of interest (LROIs) and small regions of interest (SROIs) encompassing fibroglandular tissue (FGT) and the highest signal area at the level of the nipple, respectively. The reproducibility of the measurements and correlations between density, age and FGT apparent diffusion coefficient (ADC) values were evaluatedss.ResultsNine healthy volunteers were included. The inter-reader reproducibility of TSC and FASC using SROIs and LROIs was excellent (intraclass coefficient range 0.945−0.979, p < 0.001), except for the minimum TSC LROI measurements (p = 0.369). The mean/minimum LROI TSC and mean LROI FASC values were lower than the respective SROI values (p < 0.001); the maximum LROI TSC values were higher than the SROI TSC values (p = 0.009). TSC correlated inversely with age but not with FGT ADCs. The mean and maximum FGT TSC and FASC values were higher in dense breasts in comparison to non-dense breasts (p < 0.020).ConclusionsThe chosen sampling method and the selected descriptive value affect the measured TSC and FASC values, although the inter-reader reproducibility of the measurements is in general excellent.Relevance statement23Na MRI at 3 T allows the quantification of TSC and FASC sodium concentrations. The sodium measurements should be obtained consistently in a uniform manner.Key points• 23Na MRI allows the quantification of total and fluid-attenuated sodium concentrations (TSC/FASC).• Sampling method (large/small region of interest) affects the TSC and FASC values.• Dense breasts have higher TSC and FASC values than non-dense breasts.• The inter-reader reproducibility of TSC and FASC measurements was, in general, excellent.• The results suggest the importance of stratifying the sodium measurements protocol.Graphical

Racial differences in background parenchymal enhancement, a novel marker of breast cancer.

10528 Background: Breast density is a risk factor for breast cancer that can guide eligibility for supplemental screening including MRI, but Black women are less likely to qualify for supplemental screening due to having less dense breasts on average despite higher rates of breast cancer mortality. Background parenchymal enhancement (BPE), the enhancement of fibroglandular breast tissue on contrast-enhanced MRI, is a novel biomarker that has been shown to be a superior breast cancer risk marker than breast density. Women with high BPE levels have up to a four-fold increased risk of breast cancer, even after adjusting for density, but no studies have examined BPE exclusively among Black women. Methods: We identified Black and white women ages 40-74 with no prior history of breast cancer who had a mammogram and subsequent MRI as part of a screening or diagnostic work-up from 2016-2022 at our institution. BPE was qualitatively defined according to BI-RADS (Breast Imaging Reporting and Data System), with four ordinal levels of increasing enhancement: minimal, mild, moderate, and marked. We conducted a cross-sectional study to perform a logistic regression with race as the exposure and moderate or marked BPE as the outcome, using minimal and mild BPE as the reference group, adjusted for age and BI-RADS density. Results: We included 1027 Black women and 4834 white women in our analysis. Fewer Black women had extremely dense breasts compared to white women (6% vs 13%, p= 0.04), however, there was a larger proportion of high BPE (moderate or marked) in Black women compared to white women (32% vs 28%, p = 0.03) (Table). There was an inverse association between age and BPE levels (p = 0.03), and a positive association between density and BPE levels regardless of race (p&lt; 0.01) (not shown). Black women were 39% more likely to have high BPE levels (95% CI: 1.39 [1.19, 1.63]), adjusted for age and density. Conclusions: Black women have lower density but higher BPE than white women, suggesting that BPE may improve identification of breast cancer risk for Black women. Participants [Table: see text]

Breast cancer recurrence rate after mastectomy in young females.

e12591 Background: Breast cancer is the most commonly diagnosed female cancer worldwide and the most common cause of cancer mortality in women. Surgical treatment of early breast cancer includes lumpectomy or mastectomy with or without reconstruction. Despite the surgeon’s best effort to perform a radical resection of the breast tissue during mastectomy to achieve locoregional control, residual fibroglandular tissue was found in 20% of breasts on postoperative breast MRI, which carries a risk of recurrence. Younger women with breast cancer have inferior survival rates despite receiving more aggressive therapy. Tumors in young women are more likely to be poorly differentiated, hormone receptor-negative, have a high proliferation index, and more lymphovascular invasion, which is partially explained by patients’ genetic profile. Another factor that could contribute to poor outcomes is the advanced stage at the time of diagnosis, including larger tumor size and higher rates of axillary lymph node positivity. This knowledge has motivated us to conduct this study that is dedicated to looking at the recurrence rate in young females following mastectomy. Methods: This is a retrospective study that collected data from young female patients who were diagnosed with breast cancer between January 2010 and August 2018, had an age at diagnosis of 18-45 years old, and underwent unilateral or bilateral mastectomy with or without reconstruction. The study also looked at the patient’s risk factors and genetic profile. Results: A total of 115 patients were diagnosed with early-stage breast cancer between January 2010 and August 2018 and underwent mastectomy. 81.74% were Caucasian females, and 8.7% were African American females. The mean age of diagnosis was 36.9 years. The mean BMI was 30.5 kg/m2. 59.13% of patients had a family history of breast cancer. The most common genetic mutations were BRCA1 at 7.83% and BRCA2 at 8.7%. 71% of patients underwent a bilateral mastectomy. 76% of patients had reconstruction. 77% of tumors were ER-positive, 68% PR-positive, and 26% HER2-positive. 49.4% of tumors were Nottingham grade 3. 52% of patients had no nodal involvement, while 35% had N1 disease. 34.6% had lymphovascular invasion. We found that 18/115 patients had recurrence of breast cancer following mastectomy. 5/17 patients had locoregional recurrence, while 12/17 patients had a distant recurrence; 1 patient’s recurrence data was unavailable. The median time to recurrence was 1147.5 days (335 to 3107). The 5-year recurrence free survival rate is 86% ± 3%. Conclusions: Our study found that 15.65% of young females had recurrence after mastectomy. To our knowledge, this is the first dedicated study to look at the recurrence rate after mastectomy in this young, high-risk patient population. Our study aims to encourage larger studies in young females with breast cancer to help tailor the treatment plan in this population.

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.

Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

Preliminary study of standardized semiquantitative method for ultrasonographic breast composition assessment

PurposeTo develop a classification tree via semiquantitative analysis for ultrasonographic breast composition assessment using routine breast ultrasonography examination images.MethodsThis study retrospectively enrolled 100 consecutive normal women who underwent screening mammography and supplemental ultrasonography. Based on sonographic breast composition, the patients’ breasts were classified as nondense or dense, which were correlated with mammographic breast composition. Ultrasonographic breast composition was classified based on the fibroglandular tissue (FGT) thickness-to-subcutaneous fat and retromammary fat (FAT) thickness ratio. In addition, the presence of a high glandular tissue component (GTC) in FGT or the presence of evident fat lobules in FGT was investigated. The cutoff point between the nondense and dense breasts was calculated from the area under the curve (AUC).ResultsAll cases with a high GTC were dense breasts, and all cases with evident fat lobules in the FGT were nondense breasts. The AUC of the FGT thickness-to-FAT ratio of all cases, the group without a high GTC, the group without evident fat lobules in the FGT, and the group without a high GTC or evident fat lobules in the FGT were 0.93, 0.94, 0.99, and 1, respectively.ConclusionThe presence of a high GTC indicated dense breasts, and the presence of evident fat lobules in the FGT represented nondense breasts. For the remaining cases, the cutoff point of the FGT thickness-to-FAT thickness ratio was 0.93 for ultrasonographic two-grade scale breast composition assessment with 100% accuracy.