CFS-SMO based classification of breast density using multiple texture models.

We aimed to quantitatively assess the effect of baseline breast density on the incidence, stage, and mortality, and also the natural course of the disease, considering the sensitivity of mammography to clarify its causal or masking effect. In total, 15,658 women ages 45 to 59 years from the Kopparberg randomized controlled trial in Sweden were prospectively followed from 1977 until 2004 to ascertain breast cancer incidence and death. Dense breast tissue collected at the beginning of the study was defined as pattern IV or V by the Tabár classification. Conventional risk factors were also collected at baseline. The three-state Markov model was used to estimate the preclinical incidence rate and the mean sojourn time given the fixed sensitivity. Dense breast tissue was significantly associated with breast cancer incidence [relative risk (RR)=1.57 (1.18-1.67)] and with breast cancer mortality [RR=1.91 (1.26-2.91)] after adjusting for other risk factors. Cumulative incidence rates irrespective of nonadvanced and advanced breast cancer were higher in dense breast tissue compared with nondense tissue but no difference in survival was detected between dense and nondense breast tissue. Dense breast tissue had a higher preclinical incidence rate (causal effect) and shorter mean sojourn time (masking effect) compared with nondense breast tissue by controlling the sensitivity of mammography. We corroborated the effect of baseline breast density with a higher incidence and mortality and also showed its contribution to a masking effect with long-term follow-up data. Results suggest that the screening policy with a predominantly shorter screening interval and with alternative imaging techniques might be indicated in women with dense breast tissue.

The aim of this study was to evaluate the association between insulin-like growth factor 1 receptor (IGF-1R) expression in breast cancer tissue and mammographic density and the clinical significance of IGF-1R overexpression. A total of 167 patients with primary invasive breast cancer were analyzed. Mammographic breast density and IGF-1R overexpression were correlated with clinicopathological parameters and analyzed by overall survival (OS) and disease-free survival (DFS). Increased breast tissue density was significantly associated with age, body mass index, menopausal status, histological grade and IGF-1R overexpression in the univariate analysis and with age (P=0.001), histological grade (P=0.045) and IGF-1R overexpression (P=0.021) in the multivariate analysis. IGF-1R overexpression was significantly associated with dense breast tissue in patients aged >40 years (P=0.002). IGF-1R overexpression in breast cancer in premenopausal women was associated with human epidermal growth factor receptor 2 (HER-2) positivity (P=0.016) and worse DFS (P=0.0414). There was no significant difference in OS and DFS between dense and non-dense breast tissue. IGF-1R expression in breast cancer tissue was significantly associated with mammographic breast tissue density in patients aged >40 years. It appears that IGF-1R expression in breast cancer tissue plays an important role in breast cancer in patients with dense breast tissue. In premenopausal women, IGF-1R overexpression in breast cancer tissue was significantly associated with HER-2 positivity and poor DFS.

Breast density is increasingly recognized as an independent risk factor for the development of breast cancer. It has been shown to be associated with a four-to sixfold increase a woman's risk of malignant breast disease. Increased breast density, as identified on mammography, is known to decrease the diagnostic sensitivity of the examination, which is of great concern to women at increased risk for breast cancer. Dense tissue has generally been associated with younger age and premenopausal status, with the assumption that breast density gradually decreases after menopause. However, the actual proportion of older women with dense breasts is unknown. Unfortunately, mammography is less accurate on dense breast tissue compared to fattier breast tissue. Multiple studies suggest a solution to this by demonstrating the ability of supplemental screening ultrasound to detect additional malignant lesions in women with dense breast tissue but with negative mammography. Improved screening methods for women with dense breasts are needed due to their increased risk of breast cancer and of failed early diagnosis by screening mammography.

A study using a new tool to measure mammographic density found that black women have denser breast tissue than white women. The findings add to the research on breast density, cancer disparities, and risk-based screening. Breast density refers to the amount of fibroglandular tissue in the breast when observed on a mammogram. Dense breast tissue shows up white on a mammogram, as do cancer cells, making it difficult for the radiologist to spot a tumor. About half of all women who have screening mammography are found to have dense breast tissue. About 40% of these women have heterogeneously dense breast tissue and about 10% have extremely dense breast tissue. Breast density also serves as an independent predictor of breast cancer risk. Women with heterogeneously dense breast tissue have about a 1.2 times greater risk of developing breast cancer than that of women with average breast density. Women with extremely dense breast tissue have about twice the risk. Studies comparing breast density between black women and white women have had mixed results. For the new study, researchers quantified breast density on mammography images from 1,589 black women and 1,256 white women by using a validated, publicly available, automated software algorithm called LIBRA (Laboratory for Individualized Breast Radiodensity Measurement) developed at the University of Pennsylvania. (http:// www.cbica.upenn.edu/sbia/software/ LIBRA/) The analysis controlled for other known breast cancer risk factors, including body mass index, prior biopsy, age at menarche, menopause status, family history, age at first birth, and use of menopausal hormone therapy. Black women had statistically significantly higher absolute breast area density (40.1 cm2) than white women

Diagnosis of chronic kidney disease (CKD) has mainly significant in the medical field of data mining. The objective of this paper is to predict chronic kidney disease using only nominal attributes which results compared to numerical and both nominal and numerical attributes. The classification and prediction of this paper presents correlation based feature selection (CFS) technique applied to extract important attributes and classifying them into CKD and not CKD. The CFS approach is applied on the data types of nominal, numerical and both nominal and numerical attributes in feature selection. The result of the CFS approach compared to three ranker approaches such as Information Gain, Gain Ratio, and ReliefF approach for feature selection. The accuracy of correlation based feature selection and sequential minimum optimization (CFS-SMO) approach achieved 98.5% for nominal, 95.25% for numerical and 98.5% for nominal and numerical. These experimental results declare that the correlation based feature selection (CFS) successfully extracted features from the benchmark approval of own and original chronic kidney disease (CKD) dataset and SMO classified the status of kidney disease. Therefore CFS-SMO considered as an optimistic tool to diagnosis kidney disease accurately which supports medical experts to make decision correctly.

Introduction: Breast cancer incidence is rising in premenopausal, hence, there is a critical need to understand factors underlying premenopausal breast cancer development in order to guide targeted prevention. Mammographic breast density is a strong risk factor for, as well as an intermediate phenotype for premenopausal breast cancer. Yet, the molecular mechanisms underlying the associations of dense breasts with breast cancer are not well understood. Our objectives in this study are to perform proteomic analysis in breast tissues to (i) identify proteins that are associated with breast cancer development in premenopausal women; (ii) determine which of these proteins are also associated with dense breasts. Methods: We performed proteomic analysis on tumor and adjacent normal tissues from 50 premenopausal women with breast cancer who had breast tissue samples archived at the St. Louis Breast Tumor Registry. Samples were analyzed on Orbitrap Eclipse Tribrid coupled with Vanquish Neo LC system (Thermo Fisher Scientific). Database search was performed using MaxQuant and MS1 LFQ intensities were used for further data analysis. We performed multiple imputation using Bayesian Principal Component Analysis and hierarchical clustering analysis using ‘ConsensusClusterPlus’ package in R. Pathway enrichment analysis was performed using Reactome Pathway database. 10 times repeated 5-fold cross validation using Logistic Regression (LR) was performed for model development. Top 10 proteins were selected by variable importance in logistic regression, and recursive feature elimination was performed for feature selection. We corrected for multiple testing by setting the false discovery rate, FDR, p-value < 0.05. Results: After imputing for missing values, we identified 3,571 proteins that were expressed in tumor and normal breast tissues. We selected 1,640 proteins for further analysis using median absolute deviation analysis. In multivariable-adjusted analysis, 451 proteins were up-regulated, and 180 proteins were down-regulated in tumor tissues compared to adjacent normal tissues (FDR < 0.05). 53 of the 451 proteins that were upregulated in tumor tissues were also upregulated in dense breasts. Some of the top proteins that were upregulated in both tumor tissues and dense breasts were AIMP2, ALDH18A1, BZW1, CKAP5, COPG1, ERGIC1, GSPT1, ILF2, IPO7, LRBA, and PSMD12. Mammographic breast density protein clusters were highly correlated with hormone receptor positivity clusters and were enriched in ESR-mediated signaling, IGF1R signaling, NOTCH4, PTEN regulation, and NFkB pathway. Discussion: Using untargeted proteomics, we identified proteomic signatures of breast cancer development in premenopausal women. We further identified protein biomarkers and pathways that are shared between breast tumors and dense breast tissues in premenopausal women. Our novel findings highlight mammographic breast density as a strong intermediate phenotype that can be targeted in breast cancer prevention in premenopausal women. Larger studies are needed to validate our findings. Citation Format: Shaili Tapiavala, Minsoo Son, Graham Colditz, Ah Young Goo, Adetunji Toriola. Breast Tissue Proteomic Profile of Breast Cancer in Premenopausal Women and Association with Mammographic Breast Density [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-02.

ABSTRACTInflammation is one of the hallmarks of carcinogenesis. High mammographic density has been associated with increased risk of breast cancer but the mechanisms behind are poorly understood. We evaluated whether breasts with different mammographic densities exhibited differences in the inflammatory microenvironment.Postmenopausal women attending the mammography-screening program were assessed having extreme dense, n = 20, or entirely fatty breasts (nondense), n = 19, on their regular mammograms. Thereafter, the women were invited for magnetic resonance imaging (MRI), microdialysis for the collection of extracellular molecules in situ and a core tissue biopsy for research purposes. On the MRI, lean tissue fraction (LTF) was calculated for a continuous measurement of breast density. LTF confirmed the selection from the mammograms and gave a continuous measurement of breast density. Microdialysis revealed significantly increased extracellular in vivo levels of IL-6, IL-8, vascular endothelial growth factor, and CCL5 in dense breast tissue as compared with nondense breasts. Moreover, the ratio IL-1Ra/IL-1β was decreased in dense breasts. No differences were found in levels of IL-1β, IL-1Ra, CCL2, leptin, adiponectin, or leptin:adiponectin ratio between the two breast tissue types. Significant positive correlations between LTF and the pro-inflammatory cytokines as well as between the cytokines were detected. Stainings of the core biopsies exhibited increased levels of immune cells in dense breast tissue.Our data show that dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment and, if confirmed in a larger cohort, suggests novel targets for prevention therapies for women with dense breast tissue.

About every minute a woman dies out of breast cancer, worldwide. The need for early detection cannot be overstated. Towards this, mammography is a boon for both early detection and screening of breast cancer tumors. It is an imaging system that uses low dose x-rays for examining the breasts, by the electrons reflected from the tissues. The use of screening mammography is associated with the detection of breast cancer at an earlier stage and smaller size, resulting in a reduction in mortality. This study was aimed at enhancing the current accuracy (diagnostic) of digital mammograms using industry standard simulation software tool, MATLAB and the MIAS dataset. The technique involves identification of tumor cells to segment them in terms of different stages of the disease. We consider the process of object detection, recognition and classification of mammograms with the aim of differentiating between normal and abnormal (benign or cancerous) cells. It is reported that dense breasts can make traditional mammograms more difficult to interpret. Although newer digital mammography techniques claim for better detection in dense breast tissues, the availability of such expensive digital mammograms is not widespread. This problem can be minimized by analyzing different breast structures (mammograms) using the MATLAB numerical analysis software for image processing applications. The results indicated up to 91% accuracy, compared to 70% at present. Our proposed solution has proved to be an effective way of detecting breast cancer early in different types of breast tissues.

High mammographic density and exposure to sex steroids are independent risk factors for breast cancer by yet unknown mechanisms. Inflammation is one hallmark of cancer and the tumor necrosis factor family of proteins (TNFSFs) and receptors (TNFRSFs) are key determinants of tissue inflammation. The relationship between TNFSFs/TNFRSFs and breast tissue density or local breast estradiol levels is unknown. We investigated whether TNFSFs and soluble TNFRSFs (sTNFRSFs) are dysregulated in vivo in human breast cancer and dense breast tissue of postmenopausal women. We explored TNFSF/TNFRSF correlations with breast density and estradiol, both locally in the breast and in abdominal subcutaneous (s.c.) fat as a measure of systemic effects. Microdialysis was used for local sampling of in vivo proteins and estradiol in a total of 73 women; 12 with breast cancer, 42 healthy postmenopausal women with different breast densities, and 19 healthy premenopausal women. Breast density was determined as lean tissue fraction (LTF) using magnetic resonance imaging. Microdialysis was also performed in estrogen receptor (ER) positive breast cancer in mice treated with the pure anti-estrogen fulvestrant and tumor tissue was subjected to immunohistochemistry. 23 members of the TNFSF/sTNFRSF families were quantified using proximity extension assay.Our data revealed upregulation of TNFSF10, 13 and 13B, TNFRSF6, 6B, 9, 11A, 11B, 13B, 14, and 19, and TNFR-1 and -2 in ER+ breast cancer in women. In dense breast tissue TNFSF10, 13, and 14, TNFRSF3, 6, 9, 10B, 13B, 14, 19, and TNFR-1 and -2 were upregulated. Certain TNFSFs/TNFRSFs were increased in premenopausal breasts relative to postmenopausal breasts. Furthermore, estradiol correlated with most of the TNFSF/sTNFRSF members, though LTF only correlated with some of the proteins. Several of these associations were breast tissue-specific, as very few correlated with estradiol in abdominal s.c. fat. Estrogen dependent regulations of TNFSF2 (TNF-α) and TNF-R2 were corroborated in ER+ breast cancer in mice. Taken together, our data indicate TNFSFs/sTNFRSFs may represent potential targetable pathways for treatment of breast cancer patients and in prevention of breast cancer development in women with dense breasts.

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes – taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Background The definition of an adequate surgical margin for breast cancer has been a hotly debated topic for over 20 years, with “no ink on tumor” now widely recognized as an adequate pathological margin for invasive carcinoma. Patients with dense breasts pose unique challenges in terms of accurate pre-operative evaluation of extent of disease and achieving adequate margins at initial surgery. The aim of this study therefore is to analyze re-excision rates and correlate with breast density and other clinical and pathological factors before and after the decision to accept 'no tumor at the inked margin” as an adequate margin. Methods Patients with stage I or II invasive breast cancer treated with breast conserving surgery between the 1st of June 2013 and the 31st of October 2014 were included. Patients who had surgery prior to January 1, 2014 comprise the pre-guideline group whereas those who had surgery on or after January 1, 2014 comprise the post-guideline group. Breast density was assessed by 2 independent radiologists. Inter-reader agreement was assessed using data on all study subjects and intra-reader agreement was assessed on a random sample of 121 study subjects; agreement was assessed using the kappa statistic with bootstrap confidence intervals. Logistic regression was used to model the association between breast density and re-excision, using the minimum value of breast density according to the two independent readers, within the 2 time periods. Multivariable logistic regression adjusted for patient and disease characteristics associated with re-excision on univariable analysis. Results The inter-reader agreement was 0.633 (95% confidence interval (CI): 0.604, 0.663) whereas the intra-reader agreement was 0.755 (95% CI: 0.663, 0.834). A total of 1205 patients were included, of whom 504 (41.8%) had surgery before the guideline change and 701 (58.2%) after. Overall 214 (17.8%) had at least one re-excision. The re-excision rate was significantly lower in the time period after the guideline change (15.1% verus 21.4%, p=0.006). There was no significant difference in tumor characteristics between the time periods. Younger age at diagnosis was the only clinicopathological factor that was significantly associated with increased breast density (p<0.001). On univariable analysis, increased breast density was associated with higher risk of re-excision (p=0.005), as was younger age, multifocality, presence of DCIS, HER2 status and extensive intraductal component (EIC). On multivariable analysis, time period, age at diagnosis, multifocality, presence of DCIS and EIC were significantly associated with re-excision, but breast density was not (OR 1.24, 95% CI 0.98-1.56, p=0.07). Conclusions Women who are of younger age at diagnosis are more likely to have increased breast density. Although, younger age was associated with higher rate of re-excision, we did not find breast density to be associated with a higher rate of re-excision on multivariable analysis. Citation Format: Walsh SM, Brennan SB, Zabor E, Rosenberger LH, Stempel M, Lebron-Zapata L, Gemignani ML. Does breast density increase the risk of re-excision for women with breast cancer having breast conservation therapy? [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-12-10.

Towards consensus on managing high mammographic density in population breast screening?

INTRODUCTION: High Mammographic density is a breast cancer risk factor and it is present over one third of cases of breast cancer. The 17s-hydroxysteroid dehydrogenase (17s-HSD) enzyme has possible local effects on breast tissue by stimulating growth of breast epithelial cells and is associated with mammographic density in women in pre and perimenopause. After a review in the literature, we see the importance of the enzyme 17sHSD1 in the etiology of breast cancer by both local and peripheral steroidogenesis, which may increase breast density. OBJECTIVE: For this reason we studied the correlation between mammographic breast density and immunohistochemical expression of 17s hydroxysteroid desydrogenase type 1 (17sHSD1). Casuistic and METHODS: 48 cases were evaluated with mammographic image classified by BI-RADS® system in four categories. The pattern of density 1 and 2 (measure breast tissue until 50%) was classified like low density and the pattern 3 and 4 (measure breast tissue upper 50%) like high density. Expression of the enzymes were evaluated by tissue microarray (TMA) screened with anti-17sHSD1 monoclonal rabbit antibodies (LS-C49955, Lifespan) diluted 1/200. Specimens were assigned scores on a scale from zero to three according to intensity of staining and number of stained cells. Scores zero and one were considered negative results and scores two and three indicated positive enzyme expression. RESULTS: In relation to breast density, in absolute numbers and percentages, we found the following results in category 1, 17 patients (35.42%), category 2, 17 patients (35.42%), category 3, 6 patients (12 50%) and category 4, 8 patients (16.67%). The 17sHSD1 expression was positive in 18 (37.5%). Statistical analysis showed no significant correlation between the expression of 17sHSD1 (p = 0.00048) with breast density (p = 0.666). No significant correlation between the expression of 17sHSD1 with breast density was found to be unchanged even when the analysis of the proposed subgroups (BMI greater than or less than 25, aged 50 years or less and high and low mammographic density). CONCLUSION: The results of our studies showed that there was no positive association between mammographic breast density and the expression of the enzyme 17sHSD1.

Research indicates that incorporating supplemental breast ultrasound in addition to mammography can enhance the sensitivity of public mammography screening, particularly for women with dense breast tissue. However, little information exists regarding the feasibility of integrating ultrasound into public mammography screening. Moreover, limited data exist on the awareness, perceptions, and experiences of both screening clients and healthcare providers regarding dense breast tissue, its impact on screening effectiveness, and the acceptability and implications of incorporating additional ultrasound. In a prospective, controlled mixed-methods study, 31 semi-structured telephone interviews were conducted with both screening professionals and clients identified as having dense breast tissue. Six mammography screening centres in Germany participated in the qualitative part of the DIMASOS2 study (Density-Indicated Mammographic-Sonographic Breast Cancer Screening), where additional ultrasound was offered to women aged 50 to 69 years identified as having dense breast tissue. The interviews were analysed using qualitative content analysis. Clients expressed appreciation for the additional ultrasound examination; half of them were already aware of their dense breast tissue but lacked knowledge about its implications for diagnostic accuracy. Healthcare professionals felt that diagnostic sensitivity of mammography screening needed to be improved, but also faced challenges such as insufficient evidence for adjunct measures, concerns about overdiagnosis and false positives, and substantial organisational barriers to implementing ultrasound examinations. This study highlights a gap in open discussions about breast density and its impact on screening accuracy. The absence of evidence-based information hampers informed decision making for both healthcare providers and clients. Moreover, substantial organisational constraints hinder the incorporation of breast ultrasound into public screening programme, leading to a complex and unsatisfactory situation for healthcare professionals and clients.

Introduction. Differential diagnosis of some pathological processes in the breast is difficult on the background of dense breast tissue. This often leads to false conclusions and to late diagnosis of breast cancer (BC) or unreasonable biopsy in a benign process. 50% of breast cancers detected less than 12 months after elective mammography were associated with high density of breast tissue. An important advantage of contrast enhanced spectral mammography (CESM) is that it does not depend on the size of the lesions and the X-ray density of the breast tissue. Objective. To compare the diagnostic performance of CESM and digital mammography (DM) in detection of breast cancer in a group of women with dense breast tissue.Materials and methods. The data of 438 patients with suspected breast cancer examined from August 2018 to January 2021 were analyzed in the study. The mean age of women was 50 ± 11 years (from 21 to 86 years). In the study group 154 (35%) malignant and 284 (65%) benign lesions were identified. All lesions were histologically verified. Breast tissue density corresponded to types A and B in 161 patients and corresponded to C and D types in 277 patients according to the ACR classification. 154 cases of breast cancer were identified, including 49 patients with density A and B and 105 patients with density C and D types.Results. Sensitivity, specificity and overall accuracy of DM were 85.7%, 87.3%, 86.8%, respectively. Diagnostic performance of CESM significantly higher than of DM with sensitivity, specificity and accuracy of 96.8% (p < 0.001), 93.3% (p = 0.015), 94.5% (p < 0.001), respectively. CESM had high positive and negative predictive values of 88.7% (p = 0.012) and 98.1% (p < 0.001), which exceeded those of DM – 78.6% and 91.9%, respectively. The diagnostic performance of DM and CESM were comparable in women with normal breast density (types A and B according to ACR), but in patients with high breast density (types C and D according to ACR), CESM was significantly more sensitive in detecting breast cancer.Conclusion. Thus, diagnostic efficiency of CESM in detecting breast cancer significantly higher in comparison with digital mammography.