Breast volume in non-obese females is related to breast adipose cell hypertrophy, inflammation, and COX2 expression.

BACKGROUND: Breast hypertrophy seems to be a risk factor for breast cancer and the amount and characteristics of breast adipose tissue may play important roles. The main aim of this study was to investigate associations between breast volume in normal weight women and hypertrophic adipose tissue and inflammation. METHODS: Fifteen non-obese women undergoing breast reduction surgery were examined. Breast volume was measured with plastic cups and surgery was indicated if the breast was 800 ml or larger according to Swedish guidelines. We isolated adipose cells from the breasts and ambient subcutaneous tissue to measure cell size, cell inflammation and other known markers of risk of developing breast cancer including COX2 gene activation and MAPK, a cell proliferation regulator. RESULTS: Breast adipose cell size was characterized by cell hypertrophy and closely related to breast volume. The breast adipose cells were also characterized by being pro-inflammatory with increased IL-6, IL-8, IL-1ß, CCL-2, TNF-a and an increased marker of cell senescence GLB1/ß-galactosidase, commonly increased in hypertrophic adipose tissue. The prostaglandin synthetic marker COX2 was also increased in the hypertrophic cells and COX2 has previously been shown to be an important marker of risk of developing breast cancer. Interestingly, the phosphorylation of the proliferation marker MAPK was also increased in the hypertrophic adipose cells. CONCLUSION: Taken together, these findings show that increased breast volume in non-obese women is associated with adipose cell hypertrophy and dysfunction and characterized by increased inflammation and other markers of increased risk for developing breast cancer. TRIAL REGISTRATION: Projektdatabasen FoU i VGR, project number: 249191 (https://www.researchweb.org/is/vgr/project/249191).

An investigation of tomosynthesis on the diagnostic efficacy of spot compression mammography.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer.

Significance: We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. Aim: We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. Approach: We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. Results: The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. Conclusion: The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules.

Background: The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods: This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results: The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion: The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.

[Examination of breast density in breast cancer discordant twin pairs]. / Emlodenzitás vizsgálata emlorákdiszkordáns ikerpárok esetében.

Previous twin studies show that genetic factors are responsible for 63% of the variability in breast density. We analyzed the mammographic images of 9 discordant twin pairs for breast cancer from the population-based Hungarian Twin Registry. We measured breast density using 3D Slicer software. Genetic variants predisposing to breast cancer were also examined. One of the examined twin pairs had a BRCA2 mutation in both members. There was no significant difference between the mean values of breast density in the tumor and non-tumor groups (p=0.323). In terms of parity and the presence of menopause, we found mostly no significant difference between the members of the twin pair. In our cohort of identical twins discordant for breast cancer, the average breast density showed no significant difference, which can be explained by the common genetic basis of breast cancer and breast density.

Evaluation of cosmetic outcomes in breast reconstruction patients undergoing radiotherapy using an anomaly generative adversarial network model.

Considering the rising prevalence of breast reconstruction followed by radiotherapy (RT), evaluating the cosmetic impact of RT is crucial. Currently, there are limited tools for objectively assessing cosmetic outcomes in patients who have undergone reconstruction. Therefore, we validated the cosmetic outcome using a previously developed anomaly Generative Adversarial Network (GAN)-based model and evaluated its utility. Between January 2016 and December 2020, we collected computed tomography (CT) images from 82 breast cancer patients who underwent immediate reconstruction surgery followed by radiotherapy. Among these patients, 38 received immediate implant insertion, while 44 underwent autologous breast reconstruction. Anomaly scores (AS) were estimated using an anomaly GAN model at pre-RT, 1st follow-up, 1-year (Post-1Y) and 2-year (Post-2Y) after RT. Subsequently, the scores were analyzed in a time-series manner, considering reconstruction types (implant versus autologous), RT techniques, and the incidence of major complications. The median age of the patients was 46 years (range 29-62). The AS between Post-1Y and Post-2Y demonstrated a positive relationship (coefficient 0.515, P < 0.001). The AS was significantly associated with objective cosmetic indices, namely Breast Contour Difference (P = 0.009) and Breast Area Difference (P = 0.004), at both Post-1Y and Post-2Y. Subgroup analysis stratified by type of breast reconstruction revealed significantly higher AS values in patients who underwent prosthetic implant insertion compared to those with autologous reconstruction at all follow-up time points (1st follow-up, P = 0.001; Post-1Y, P < 0.001; and Post-2Y, P < 0.001). A threshold AS of ≥ 1.9 was associated with a 10% predicted risk of developing major complications. The feasibility of an AS generated by a GAN model for predicting both cosmetic outcomes and the likelihood of complications following RT has been successfully validated. Further investigation involving a larger patient cohort is warranted.

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

BACKGROUND: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). METHODS: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. RESULTS: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(ß) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(ß) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(ß) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(ß) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(ß) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). CONCLUSIONS: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

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

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

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

BACKGROUND: Residual 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. METHODS: We 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. RESULTS: Significantly 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. CONCLUSION: Mastectomy 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.

Comparison of staining quality between rapid and routine hematoxylin and eosin staining of frozen breast tissue sections: an observational study.

OBJECTIVE: To compare the staining quality between rapid hematoxylin and eosin (H&E) staining and routine H&E staining of frozen breast tissue sections. METHODS: In this cross-sectional observational study, 120 frozen breast tissue sections were randomly assigned to rapid or routine H&E staining (n = 60 per group). Rapid H&E staining used a 7:1 mixture of modified Gill's hematoxylin and alcohol-soluble 1% eosin Y. The staining quality of each section was evaluated and scored. A score of >7 was considered excellent, a score of 6 to 7 good, and a score of ≤5 poor. RESULTS: The staining time for rapid staining was approximately 3 minutes, whereas that of routine staining was approximately 12 minutes. There were no significant differences in the staining quality scores or proportions of sections in each grade between the two staining methods. The proportions of sections that were classified as excellent or good were 96.7% and 98.3% for rapid and routine staining, respectively. CONCLUSIONS: In frozen breast tissue sections, rapid H&E staining may provide staining quality that is comparable to that of routine staining, while markedly reducing the staining time.

Mammographic breast features and risk of cardiovascular diseases in korean women.

BACKGROUND: There is a growing amount of evidence on the association between cardiovascular diseases (CVDs) and breast calcification. Thus, mammographic breast features have recently gained attention as CVD predictors. OBJECTIVE: This study assessed the association of mammographic features, including benign calcification, microcalcification, and breast density, with cardiovascular diseases. METHODS: This study comprised 6,878,686 women aged ≥40 who underwent mammographic screening between 2009 and 2012 with follow-up until 2020. The mammographic features included benign calcification, microcalcification, and breast density. The cardiovascular diseases associated with the mammographic features were assessed using logistic regression. RESULTS: The prevalence of benign calcification, microcalcification, and dense breasts were 9.6 %, 0.9 % and 47.3 % at baseline, respectively. Over a median follow-up of 10 years, benign calcification and microcalcification were positively associated with an increased risk of chronic ischaemic heart disease whereas breast density was inversely associated with it; the corresponding aOR (95 % CI) was 1.14 (1.10-1.17), 1.19 (1.03-1.15), and 0.88 (0.85-0.90), respectively. A significantly increased risk of chronic ischaemic heart disease (IHD) was observed among women with benign calcifications (aHR, 1.14; 95 % CI 1.10-1.17) and microcalcifications (aOR, 1.19; 95 % CI 1.06-1.33). Women with microcalcifications had a 1.16-fold (95 % CI 1.03-1.30) increased risk of heart failure. CONCLUSIONS: Mammographic calcifications were associated with an increased risk of chronic ischaemic heart diseases, whereas dense breast was associated with a decreased risk of cardiovascular disease. Thus, the mammographic features identified on breast cancer screening may provide an opportunity for cardiovascular disease risk identification and prevention.

Clinical application and effect evaluation of acupoint thread embedding therapy and traditional Chinese medicine treatment based on menstrual cycle characteristics in the management of breast hyperplasia: An observational study.

To evaluate the effectiveness of the combination of acupoint embedding therapy and traditional Chinese medicine dialectical treatment regimen in improving clinical symptoms, promoting tumor regression, controlling adverse reactions and complications, and enhancing patient satisfaction by comparing and analyzing the clinical data of 120 breast tumor patients. One hundred twenty patients with breast cancer were divided into a treatment group (60 cases) and a control group (60 cases) according to different treatment plans. Patients in the treatment group received a combination of acupoint embedding therapy and traditional Chinese medicine dialectical treatment based on different time points of the menstrual cycle. Including the proportion of reduction in the number of breast masses, the proportion of reduction in mass size, changes in pain severity scores, tumor regression rate, regression time, incidence of adverse reactions and complications, and patient satisfaction. Statistical software was used to analyze the data to evaluate differences between the 2 groups. In terms of clinical symptoms, the proportion of reduction in the number of breast masses in the treatment group averaged 50%, significantly higher than the 25% in the control group; the proportion of reduction in mass size averaged 40%, also higher than the 15% in the control group; and the improvement in pain severity scores was also superior to the control group. Regarding tumor regression, the tumor regression rate in the treatment group reached 85%, with an average regression time of 6.2 weeks, both significantly better than the 55% and 9.8 weeks in the control group. In terms of adverse reactions and complications, the incidence rate in the treatment group was relatively low, and no serious adverse events occurred. Patient satisfaction surveys showed that the treatment group had significantly higher satisfaction with treatment effectiveness, treatment process, and physician service attitude compared to the control group. Based on clinical data from 120 breast tumor patients, the results of this study indicate that breast tumor patients treated with a specific treatment regimen have significant advantages in improving clinical symptoms, tumor regression, controlling adverse reactions and complications, and patient satisfaction. This treatment regimen has high clinical application value and deserves further promotion.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.

BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. METHODS: A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. RESULTS: Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). CONCLUSIONS: DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Specificities of mammary and periprostatic adipose tissues: A perspective from cancer research.

In addition to the major subcutaneous and visceral adipose tissues (AT), other adipose depots are dispersed throughout the body and are found in close interaction with proximal organs such as mammary and periprostatic AT (MAT and PPAT respectively). These ATs have an effect on proximal organ function during physiological processes and diseases such as cancer. We highlighted here some of their most distinctive features in terms of tissular organization and responses to external stimuli and discussed how obesity affects them based on our current knowledge.

Value of sonoelastography for diagnosis of breast non-mass lesions and comparison with BI-RADS: A systematic review and meta-analysis.

BACKGROUND: Not all the breast lesions were mass-like, some were non-mass-like at ultrasonography. In these lesions, conventional ultrasonography had a high sensitivity but a low specificity. Sonoelastography can evaluate tissue stiffness to differentiate malignant masses from benign ones. Then what about the non-mass lesions? The aim of this study was to evaluate the current accuracy of sonoelastography in the breast non-mass lesions and compare the results with those of the American College of Radiology breast Imaging-Reporting and Data System (BI-RADS). METHODS: An independent literature search of English medical databases, including PubMed, Web of Science, Embase & MEDLINE (Embase.com) and Cochrane Library, was performed by 2 researchers. The accuracy of sonoelastography was calculated and compared with those of BI-RADS. RESULTS: Fourteen relevant studies including 1058 breast non-mass lesions were included. Sonoelastography showed a pooled sensitivity of 0.74 (95% CI: 0.70-0.78), specificity of 0.89 (95% CI: 0.85-0.91), diagnostic odds ratio (DOR) of 25.22 (95% CI: 17.71-35.92), and an area under the curve of 0.9042. Eight articles included both sonoelastography and BI-RADS. The pooled sensitivity, specificity, DOR and AUC were 0.69 versus 0.91 (P < .01), 0.90 versus 0.68 (P < .01), 19.65 versus 29.34 (P > .05), and 0.8685 versus 0.9327 (P > .05), respectively. CONCLUSIONS: Sonoelastography has a higher specificity and a lower sensitivity for differential diagnosis between malignant and benign breast non-mass lesions compared with BI-RADS, although there were no differences in AUC between them.

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

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

Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning.

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

Role of oedema and shrinkage patterns for prediction of response to neoadjuvant chemotherapy and survival outcomes in luminal breast cancer.

AIMS: To explore the independent and additional value of oedema and shrinkage patterns for predicting the disease-free survival (DFS) and neoadjuvant chemotherapy (NAC) response in luminal breast cancer (BC). MATERIALS AND METHODS: Patients with luminal BC who underwent NAC were enrolled in this study from 2017 to 2022. Traditional MRI features include BI-RADS-based MRI descriptors, tumor size, and ADC values, while emerging MRI features include oedema and shrinkage patterns, all of which were evaluated before, early, and after NAC. The changes in features during NAC were also evaluated. The value of features was evaluated through univariate, multivariate analyses. RESULTS: A total of 258 patients were enrolled in this study, of which 77 responded to NAC. Diffuse oedema, stable or increased oedema during early NAC were adverse predictors for treatment response, while a greater reduction in tumor size and increase in ADC value were favorable predictors (all P<0.05). Furthermore, 20 of 60 patients who were followed up experienced recurrence. Diffuse oedema, pre-pectoral or subcutaneous oedema, and non-concentric shrinkage patterns after NAC were risk factors for DFS, whereas a greater increase in ADC value was a protective factor. Incorporating oedema and shrinkage patterns into traditional MRI features improved the predictive performance for treatment response (AUC from 0.76-0.78 to 0.80-0.83) and DFS (C-index from 0.67-69 to 0.75-0.80). CONCLUSIONS: Oedema is an unfavorable predictor for treatment response and survival outcomes, while shrinkage patterns contribute more to the prognostic value, both of which could offer supplementary benefits for clinical outcomes in luminal BC.