Breast density

We aimed to determine which combination of physical examination (pe), mammography (mam), and ultrasonography (us) would optimize breast cancer detection in China. We conducted a trial of screening with pe, mam, and us among Chinese women 25 years of age and older. All initial screenings using the three modalities were completed within 30 days of each other, and subjects were followed approximately 1 year later. The performances of the three screening methods used alone, in parallel, or in series were compared. Data were analyzed using exact confidence intervals (cis) and the McNemar test. Between March 2009 and July 2011, 3028 eligible women completed all study examinations. At a mean follow-up of 1.3 years, 33 breast cancers were identified in the study population. Mammography detected 28 cancers; us, 24 cancers; and pe, 22 cancers. During the follow-up period, 2 false-negative cases occurred clinically. The highest sensitivity for breast cancer screening (93.9%) was achieved by pa...

Conventional mammographic dosimetry has been developed over the past forty years. Prior to the availability of high resolution three-dimensional breast images, certain assumptions about breast anatomy were required. These assumptions were based upon the information evident on twodimensional mammograms; they included assumptions of thick skin, a uniform mixture of glandular and adipose tissue, and a median breast density of 50%. Recently, the availability of high-resolution breast CT studies has provided more accurate data about breast anatomy, and this, in turn, has provided the opportunity to update mammographic dosimetry. Based on hundreds of data sets on breast CT volume, a number of studies were performed and reported which have shed light on the basic breast anatomy specific to dosimetry in mammography. It was shown that the average skin thickness of the breast was approximately 1.5 mm, instead of the 4 or 5 mm in the past. In another study, 3-D breast CT data sets were used for validation of the 2-D algorithm developed at the University of Toronto, leading to data suggesting that the overall average breast density is of the order of 16-20%, rather than the previously assumed 50%. Both of these assumptions led to normalized glandular dose (DgN) coefficients which are higher than those of the past. However, a comprehensive study on hundreds of breast CT data sets confirmed the findings of other investigators that there is a more centralized average location of glandular tissue within the breast. Combined with Monte Carlo studies for dosimetry, when accurate models of the distribution of glandular tissue were used, a 30% reduction in the radiation dose (as determined by the DgN coefficient) was found as an average across typical molybdenum and tungsten spectra used clinically. The 30% average reduction was found even when the thinner skin and the lower average breast density were considered. The article reviews three specific anatomic observations made possible based on high-resolution breast CT data by several different research groups. It is noted that, periodically, previous assumptions pertaining to dosimetry can be updated when new information becomes available, so that more accurate dosimetry is achieved. Dogmatic practices typically change slowly, but it is hoped that the medical physics community will continue to evaluate changes in DgN coefficients such that they become more accurate.

In mammographic imaging, the ®lm area corresponding to the breast periphery is overexposed, resulting in high optical density and degraded contrast in this region. A digital, model-driven density equalization technique was designed and developed to overcome this overexposure problem, taking into account the non-linear characteristic curve of the ®lm-digitizer system. The method is based on several image processing and analysis techniques, such as thresholding, which is used to segment the pixels of the mammogram belonging to the breast region from the background, and wavelet-based fusion, which is used to equalize the pixels of breast periphery selectively while leaving the remaining breast region unaffected. Initial application of the method resulted in density-equalized mammographic images, characterized by improved contrast at the breast periphery.

Authors regret to inform that, due to an unfortunate oversight, some data from the Swiss breast screening programme was omitted. The second and third sentences of the second paragraph of the Results should read. Of the 29 countries approached, 8 programs recorded the use of prescribed HT at each screening episode (Table ). Five of the eight also sought information about ever use of HT with four of the five, and one other, recording whether combined HT or progestin alone was prescribed. The corrected Tables and are given as follows.

Purpose: To assess whether subjective breast density categorization remains the most useful way to categorize mammographic breast density and whether variations exist across geographic regions with differing national legislation. Methods: Breast radiologists from two countries (UK, USA) were voluntarily recruited to review sets of anonymized mammographic images (n ¼ 180) and additional repeated images (n ¼ 70), totaling 250 images, to subjectively rate breast density according to the Breast Imaging Reporting and Data system (BI-RADS) categorization. Images were reviewed using standardized viewing conditions and Ziltron software. Inter-rater reliability was analyzed using the Kappa test. The US radiologists (n ¼ 25) judged fewer images as being ''mostly fatty'' than UK radiologists (n ¼ 24), leading a greater number of images classified in the higher BI-RADS categories, particularly in BI-RADS 3. Overall agreement for all data sets was k ¼ 0.654 indicating substantial agreement between the two cohorts. When the data were split into BI-RADS categories, the level of agreement varied from fair to substantial. Variations in how radiologists from the USA and UK classify breast density was established, especially when the data were divided into breast density categories. This variation supports the need for a reliable breast density assessment method to enhance the individualized supplemental screening pathways for dense breasts. The use of two-scale categorization method demonstrated improved agreement.

Objective: To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. Methods: Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50614 years; mean body mass index: 2263) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity © , developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. Results: The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p,0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r50.56, p,0.01 and r50.48, p,0.01, respectively). Conclusion: Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. Advances in knowledge: On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

BACKGROUND: Breast cancer is the most commonly diagnosed malignancy in women worldwide. Mammography is the best screening method for the early detection of breast cancer in asymptomatic women; however, its results are associated with high false positive/negative rates. One factor that decreases the sensitivity of mammography-based diagnosis of breast cancer is breast density. This study aimed to identify the association between mammographically determined breast density and breast cancer.METHODS: This single-center retrospective study included 2158 female participants in the Eastern Province of Saudi Arabia who underwent mammography between 2016 and 2018. We evaluated baseline information on breast density and its relationship with breast cancer risk and other factors (age, age at first childbirth, menopause status, and family breast cancer history). Mammogram reports were categorized using the Breast Imaging Reporting and Data System lexicon. RESULTS: The majority (39%) of particip...

Contrast-enhanced mammography (CEM) is increasingly used in breast cancer diagnostics, especially for women with dense breast tissue. However, interpreting background parenchymal enhancement (BPE)-a normal tissue reaction to contrast-is still subjective due to the lack of a standardized system. This study introduces the Breast Contrast Standard Scale (BCSS), a new four-level system designed specifically for CEM to assess BPE consistently. The results show that higher breast density is linked to stronger BPE, and the proposed scale demonstrated excellent agreement between different radiologists. Standardizing BPE assessment could improve diagnostic accuracy and support personalized screening strategies in clinical practice.

Abstract: The objective of this project was to determine the mean glandular dose (MGD) from Craniocaudal (CC) and Mediolateral Oblique (MLO) views to the breast during diagnostic mammography and the total dose per woman. The study was conducted at the ...

Objectives To compare breast density measured on digital breast tomosynthesis (DBT) (BI-RADS-based breast composition and fully-automatic estimation) and magnetic resonance imaging (MRI) (BI-RADS amount of fibroglandular tissue), and to evaluate the diagnostic performance in terms of sensitivity and specificity of DBT and MRI in a predominantly dense breast population. Methods Between 2015 and 2016, 152 women with 103 breast malignancies, who underwent 3-T breast MRI and DBT within 2 months' time, were enrolled in this study. Breast composition/fibroglandular tissue and findings on DBT (two readers) and MRI were reported using BI-RADS 5th edition. Digital mammography images were analysed for breast percent density (PD) using the Libra software tool. Results A majority of women had dense breasts as categorised by breast composition c (heterogeneously dense) (68%) and d (extremely dense) (15%). The mean PD was 44% (range, 18-89%) and the correlation between breast composition and PD was r = 0.6. The diagnostic performance of MRI was significantly higher compared to DBT for one reader as described by the area under the receiver operating characteristic (ROC) curve (p = 0.004) and of borderline significance for the other reader (p = 0.052). Conclusions MRI had higher diagnostic performance than DBT in a dense breast population in the tertiary setting. Key Points • MRI had higher diagnostic performance than DBT in a dense breast population • Diagnostic performance of DBT was comparable to MRI in women with fatty breasts • MRI was superior to DBT in preoperative breast cancer size assessment Keywords Breast cancer . Breast density . Digital breast tomosynthesis . Magnetic resonance imaging . Diagnostic techniques and procedures Abbreviations DBT Digital breast tomosynthesis PD Percent density DM Digital mammography FGT Fibroglandular tissue BPE Background parenchymal enhancement FN False negative NST No special type DCIS Ductal carcinoma in situ ILC Invasive lobular carcinoma FP False positive * Daniel Förnvik

Introduction: Mammographic density (MD) is the area of breast tissue that appears radiologically white on mammography. Although high MD is a strong risk factor for breast cancer, independent of BRCA1/2 mutation status, the molecular basis of high MD and its associated breast cancer risk is poorly understood. MD studies will benefit from an animal model, where hormonal, gene and drug perturbations on MD can be measured in a preclinical context. Methods: High and low MD tissues were selectively sampled by stereotactic biopsy from operative specimens of high-risk women undergoing prophylactic mastectomy. The high and low MD tissues were transferred into separate vascularised biochambers in the groins of SCID mice. Chamber material was harvested at six weeks for histological analyses, and immunohistochemistry for cytokeratins, vimentin and a human-specific mitochondrial antigen. Within-individual analysis was performed in replicate mice, eliminating confounding by age, body mass index and process-related factors, and comparisons were made to the parental human tissue. Maintenance of differential MD post-propagation was assessed radiographically. Results: Immunohistochemical staining confirmed the preservation of human glandular and stromal components in the murine biochambers, with maintenance of radiographic MD differential. Propagated high MD regions had higher stromal (p=0.0002) and lower adipose (p=0.0006) composition, reflecting the findings in the original human breast tissue, although glands appeared small and non-complex in both groups. No significant differences were observed in glandular area (p=0.4) or count (p=0.4) between high and low MD biochamber tissues. Human mammary glandular and stromal tissues were viably maintained in murine biochambers, with preservation of differential radiographic density and histological features. Our study provides a murine model for future studies into the biomolecular basis of MD as a risk factor for breast cancer.

Background: Estimating the size and volume of the breast preoperatively is an important step in surgical planning for many breast procedures such as immediate implant-based breast reconstructions and reduction mammoplasties. Breast volume estimation helps in appropriate implant selection preoperatively. Objectives: The aim of this study was to objectively evaluate the estimation of breast weight by automatic volumetric breast assessment in digital breast tomosynthesis (DBT) using Quantra™ 2.2 Breast Density

Existing literature on inter‐rater reliability focuses on quantifying the disagreement between raters. In this paper, we introduce a method to correct for inter‐rater disagreement (or observer bias), where raters are assigning scores on a continuous scale. To do this, we propose a two‐stage approach. In the first stage, we standardise the distributions of rater scores to account for each rater's subjective interpretation of the continuous scale. In the second stage, we correct for case‐mix differences between raters by exploiting pairwise information where two raters have read the same entity on a case. We illustrate the use of our procedure on clinicians’ visual assessments of breast density (a risk factor for breast cancer). After applying our procedure, 229 out of 1398 women who were originally classified as high density were re‐classified as non‐high density, and 382 out of 12 348 women were re‐classified from non‐high to high density. A simulation study also demonstrates go...

Objectives: The association between breast cancer (BC) and thyroid disorders has been widely explored with unclear results. Mammographic density (MD) is one of the strongest risk factor for BC. This study explores the relationship between thyroid diseases and MD in Spanish women. Materials & methods: This cross-sectional study covered 2883 women aged 47e71 years participating in 7 BC screening programs in 2010. They allowed access to their mammograms, had anthropometricalmeasures taken, and answered a telephonic epidemiological interview which included specific questions on thyroid diseases. Percentage of MD was assessed with a semiautomatic-computer tool (DMscan) by two trained radiologists. We calculated the geometric mean of MD percentages (mean MD). Multivariable mixed linear regression models with random screening-center-specific intercepts were fitted, using log-transformed percentage of MD as dependent variable and adjusting for age, body mass index, menopausal status and other confounders. e b represents the relative increase of mean MD. Results: 13.9% of the participants reported personal history of thyroid disease. MD was not associated to hyperthyroidism (e b :1.05, 95%CI: 0.82e1.36), hypothyroidism (e b :1.02, 95%CI: 0.75e1.38), thyroid nodules (e b :1.01, 95%CI: 0.85e1.19) or thyroid cancer (e b :1.03, 95%CI: 0.56e1.92). However, women with goiter had lower MD (mean MD no-goiter : 13.4% vs mean MD goiter : 10.6%; e b :0.79, 95%CI: 0.64e0.98) and those with Hashimoto thyroiditis had higher MD (mean MD no-thyroiditis : 13.3% vs mean MD thyroidits : 25.8%; e b :1.94, 95%CI: 1.00e3.77). Conclusion: Functional thyroid disorders were not related to MD. However, MD was lower in women with goiter and higher in those reporting Hashimoto's thyroiditis. These relationships should be confirmed in future studies.

Background: Increased mammographic breast density is a moderate risk factor for breast cancer. Different scales have been proposed for classifying mammographic density. This study sought to assess intra-rater agreement for the most widely used scales (Wolfe, Tabár, BI-RADS and Boyd) and compare them in terms of classifying mammograms as high-or low-density. The study covered 3572 mammograms drawn from women included in the DDM-Spain study, carriedout in seven Spanish Autonomous Regions. Each mammogram was read by an expert radiologist and classified using the Wolfe, Tabár, BI-RADS and Boyd scales. In addition, 375 mammograms randomly selected were read a second time to estimate intra-rater agreement for each scale using the kappa statistic. Owing to the ordinal nature of the scales, weighted kappa was computed. The entire set of mammograms (3572) was used to calculate agreement among the different scales in classifying high/low-density patterns, with the kappa statistic being computed on a pair-wise basis. High density was defined as follows: percentage of dense tissue greater than 50% for the Boyd, "heterogeneously dense and extremely dense" categories for the BI-RADS, categories P2 and DY for the Wolfe, and categories IV and V for the Tabár scales. Results: There was good agreement between the first and second reading, with weighted kappa values of 0.84 for Wolfe, 0.71 for Tabár, 0.90 for BI-RADS, and 0.92 for Boyd scale. Furthermore, there was substantial agreement among the different scales in classifying high-versus low-density patterns. Agreement was almost perfect between the quantitative scales, Boyd and BI-RADS, and good for those based on the observed pattern, i.e., Tabár and Wolfe (kappa 0.81). Agreement was lower when comparing a pattern-based (Wolfe or Tabár) versus a quantitative-based (BI-RADS or Boyd) scale. Moreover, the Wolfe and Tabár scales classified more mammograms in the high-risk group, 46.61 and 37.32% respectively, while this percentage was lower for the quantitative scales (21.89% for BI-RADS and 21.86% for Boyd). Conclusions: Visual scales of mammographic density show a high reproducibility when appropriate training is provided. Their ability to distinguish between high and low risk render them useful for routine use by breast cancer screening programs. Quantitative-based scales are more specific than pattern-based scales in classifying populations in the high-risk group.

Objective: To examine the association between two dietary patterns (Western and Mediterranean), previously linked to breast cancer risk, and mammographic density. Methods: This cross-sectional study included 3584 women attending population-based breast cancer screening programs and recruited between October 7th, 2007 and July 14th, 2008 (participation rate: 74.5%). Collected data included anthropometric measurements, demographic, obstetric and gynecologic characteristics, family and personal health history, and diet in the preceding

OBJETIVO: Identificar los principales hallazgos histopatológicos benignos y determinar la tasa de falsos positivos que suelen causar conflicto al categorizar las mastografías en el sistema BI-RADS por su aspecto, que puede simular un proceso maligno. MATERIALES Y MÉTODOS: Estudio de cohorte, retrospectivo, efectuado en pacientes atendidas en la Unidad Médica de Alta Especialidad 4 Luis Castelazo Ayala (2019-2023) con reporte mastográfico alterado o sospecha clínica de malignidad. Para el análisis estadístico se utilizó el programa JASP 2.0 y χ 2 para la diferencia de proporciones entre grupos. RESULTADOS: De un grupo de 11,481 pacientes, se reportaron 1643 mastografías alteradas: 444 con reportes falsos positivos, 23 pacientes con sospecha clínica y exclusión de 16 que no cumplieron con los criterios de inclusión establecidos. La muestra poblacional estudiada fue de 451 pacientes. La mayoría permaneció asintomática al momento del estudio (42.1%). El hallazgo histopatológico benigno con mayor prevalencia fue el fibroadenoma y su síntoma más relevante el nódulo palpable. La tasa de falsos positivos fue de 4.3%. CONCLUSIONES: En la actualidad, gracias a la implementación de programas de tamizaje es posible establecer diagnósticos de cáncer de mama en etapas tempranas, aunque con la desventaja que el reporte puede resultar falso positivo y ello dar lugar a incremento de la morbilidad y sobretratamiento. Los estándares internacionales indican que estos no deben sobrepasar el 10%.

El cáncer de mama continúa siendo un problema de salud pública a nivel global. Se estima que su incidencia incrementará en los próximos años. Es importante evaluar a través de indicadores los estudios de escrutinio de las clínicas de mastología con el fin de obtener una adecuada correlación clínica, imagenológica e histopatológica. Objetivos. Evaluar la correlación que existe entre la clínica, los estudios radiológicos e histopatológicos en mujeres que acudieron a exámenes de escrutinio para cáncer de mama en un periodo de 10 años. Métodos. Se realizó un estudio transversal, descriptivo de cohorte retrospectiva efectuado de los registros de un centro de referencia en el periodo de junio del 2013 a junio 2023, de mujeres quienes acudieron a tamizaje para cáncer de mama. En aquellas que se les realizó toma de biopsia, las muestras fueron analizadas en nuestra institución. Resultados. Se analizaron los registros de 6 754 mujeres que cumplieron los criterios de inclusión, en donde la edad promedio de la población de estudio fue de 50,7 años. Se registró que 73,2% de los estudios se categorizaron BI-RADS 2 y la mayoría de las participantes cursaba asintomática. De aquellas 551 mujeres que recibieron una mamografía BI-RADS >4, 226 (41%) obtuvieron un resultado de malignidad. Conclusiones. La mamografía ha demostrado ser la única herramienta de imagen que logrado tener un impacto sobre la mortalidad. Existen modalidades, como la tomosíntesis, que han incrementado el rendimiento de esta prueba. Es válido auxiliarse de nuevas tecnologías para mantener estándares de calidad en los estudios.

Background 18 F-FDG uptake PEM (positron emission mammography) Mammographic density Breast MRI Background parenchymal enhancement (BPE) a b s t r a c t We aimed to determine whether background 18 F-FDG uptake in positron emission mammography (PEM) was related to mammographic density or background parenchymal enhancement in breast MRI. Methods: We studied a total of 52 patients (mean age, 50.9 years, 26 premenopausal, 26 postmenopausal) with newly diagnosed breast cancer who underwent 18 F-FDG PEM (positron emission mammography), conventional mammography and breast MRI. The background mean 18 F-FDG uptake value on PEM was obtained by drawing a user-defined region of interest (ROI) in a normal area of the contralateral breast. We reviewed the mammography retrospectively for overall breast density of contralateral breast according to the four-point scale (grade 1-4) of the Breast Imaging Reporting and Data System (BI-RADS) classification. The background parenchymal enhancement of breast MRI was classified as minimal, mild, moderate, or marked. All imaging findings were interpreted by two readers in consensus without knowledge of image findings of other modalities. Results: Multiple linear regression analysis revealed a significant correlation between background 18 F-FDG uptake on PEM and mammographic density after adjustment for age and menopausal status (P < 0.01), but not between background 18 F-FDG uptake on PEM and background parenchymal enhancement on MRI. Conclusion: Background 18 F-FDG uptake on PEM significantly increases as mammographic density increases. Background parenchymal enhancement in breast MRI was not an independent predictor of the background 18 F-FDG uptake on PEM unlike mammographic density.

Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.

In the present study, we measured breast density by using a fully automated software and cross-compared density distribution functions of BI-RADS categories. Our final goal was to understand to what extent the new BI-RADS categories represent mammographic density distribution. The density distributions of the BI-RADS categories measured with a publically available software tool show that: category A dominates up to 11% with the probability P~0.90 to have such density; category B dominates in the interval between 11 and 17% with P~0.25 to have a density within this region; category C predominate between 17 and 32% with the probability P~0.53 to find breast inside this interval; category D is dominant for densities higher than 32% with P~0.86 to have such density.

Purpose: Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two-dimensional projections from mammography with or without tomosynthesis using either (1) subjective assessment, or (2) a computer-aided approach. The purpose of this work is two-fold: (1) to establish an algorithm for quantitative assessment of breast density using quantitative three-dimensional transmission ultrasound imaging; and (2) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density. Methods: We described and verified a threshold-based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating This article is protected by copyright. All rights reserved. fibro-glandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments. Results: QBD measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between quantitative breast density and both subjective mammographic assessments of BI-RADS breast composition categories and Volpara density scoresthe Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71 -0.96) and 0.96 (95% CI: 0.92-0.98), respectively. Conclusions: The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and FDA-approved objective assessments of breast density.

Elevated breast density is among the strongest independent predictors of breast cancer. Breast density scores are critical inputs in models used to calculate a patient&#39;s lifetime risk of developing breast cancer. Today, the only FDA-cleared technology for assessing breast density uses mammography. An alternative modality for breast density quantification is 3D transmission ultrasound (TU). In this retrospective study, we compared automated breast density calculations derived from TU using quantitative breast density (QBD) and mammography with tomosynthesis using VolparaDensity 3.1 for 225 breasts. Pearson correlation coefficients (r) and intraclass correlation coefficients were compared. Subset analyses of extremely dense breasts, premenopausal, and postmenopausal breasts were also performed. Comparative analysis between radiologist-derived density assessment and objective automated scores was performed. Calculations from TU and mammography with tomosynthesis for breast density,...

Purpose: Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two-dimensional projections from mammography with or without tomosynthesis using either (1) subjective assessment, or (2) a computer-aided approach. The purpose of this work is two-fold: (1) to establish an algorithm for quantitative assessment of breast density using quantitative three-dimensional transmission ultrasound imaging; and (2) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density. Methods: We described and verified a threshold-based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating This article is protected by copyright. All rights reserved. fibro-glandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments. Results: QBD measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between quantitative breast density and both subjective mammographic assessments of BI-RADS breast composition categories and Volpara density scoresthe Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71 -0.96) and 0.96 (95% CI: 0.92-0.98), respectively. Conclusions: The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and FDA-approved objective assessments of breast density.

Background: Mammography is an approved screening technique which may reduce breast cancer mortality. Evidences suggest that women with high breast density are four to five times more likely to get breast cancer than women with low breast density. In order to examine the effect of several factors on mammographic density, we designed and conducted this study in our breast clinic. Methods: In this cross sectional study a structured questionnaire consisting of several variables such as age, reproductive index, BMI, OCP consumption, age of menarche, breast cup size was fulfilled. Completed data were analyzed and their correlation with breast density was studied. We used chi square test and logistic regression for determining the relationship between the covariates and density of breast tissue variable. Results: In total 428 questionnaires were reviewed. About 65% of populations (277 women) were Under the age of 50 year and 44% of them were more than 50. The results indicate that there is...

Objectives To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. Methods Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). Results Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). Conclusions Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Objectives: To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. : Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from 9 centers and 6 different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. Panel majority report (PMR) was used as reference standard. Agreement () and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification. Results: While the agreement of individual radiologists with PMR ranged from =0.483 to =0.885, the ABDE correctly classified 563/613 mammograms (92%). A substantial agreement for binary classification was found for individual reader pairs (=0.620, standard deviation [SD]=0.140), individual versus PMR (=0.736, SD=0.117), and individual versus ABDE (=0.674, SD=0.095). Agreement between ABDE and PMR was almost perfect (=0.831). The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation.

Introduction: Breast cancer is a significant global health issue that requires ongoing research into potential etiological causes. The human papillomavirus (HPV) is a pathogen linked to several cancers, raising questions about its potential role in the development of breast cancer. Research into previously unexplored areas, beyond the known risk factors, has increased due to the recognition of breast cancer as a major challenge in modern medicine. While HPV is well-established for its association with cervical cancer—especially high-risk subtypes like HPV 16 and 18, which are strongly linked to premalignant and invasive lesions in the anogenital and oropharyngeal regions—recent research suggests a potential new connection between HPV and breast cancer. This is supported by the discovery of Koilocytes in breast cancer cases. Additionally, while the HPV vaccine has been proven effective in preventing cervical, head, and neck cancers, the link between HPV and breast cancer remains an emerging area of investigation.

Materials and Methods: The study employs a literature review design. Two independent reviewers conducted a comprehensive search using Medline, Scopus, Embase, and Web of Science. The search was guided by MeSH terms related to HPV, vaccines, and breast cancer. Inclusion criteria focused on studies involving breast cancer patients and the association between HPV vaccines and breast cancer. Of 233 initially identified articles, 11 met the eligibility criteria.

Conclusion: Breast cancer, the second most common cancer worldwide, has prompted research into various etiological factors. Although HPV is traditionally linked to cervical cancer, recent findings, such as the presence of Koilocytes in breast cancer, suggest a potential connection. This literature review highlights the complex interplay between HPV and breast cancer, though the role of HPV vaccinations in breast cancer prevention remains inconclusive. Continued research, including larger cohorts and molecular analyses, is essential to fully understand the relationship between HPV and breast cancer.

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a "medical biobank consortium" to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine.

Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40–85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available “LIBRA” software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significan...

Background: The effect of changes in mammographic density over time on the risk of breast cancer remains inconclusive. Methods: We used information from four centers of the Breast Cancer Screening Program in Spain in the period 1996-2015. We analyzed individual level data from 117,388 women first screened age 50-54, with at least two screening examinations. Breast density was determined using the BI-RADS classification (A to D in increasing order) at earliest and latest screening examination. Adjusted Poisson regression models were used to estimate the relative risk (RR) and 95% confidence intervals (95%CI) of the association between changes in mammographic density and breast cancer risk over time. Results: During an average 5.8 years of follow-up, 1592 (1.36%) women had a breast cancer diagnosis. An increase in density category increased breast cancer risk, and a decrease in density decreased the risk, compared with women who remained in the same BI-RADS category. Women whose density category increased from B to C or B to D had a RR of 1.55 (95%CI= 1.24-1.94) and 2.32 (95%CI= 1.48-3.63), respectively. The RR for women whose density increased from C to D was 1.51 (95%CI= 1.03-2.22). Changes in BI-RADS density were similarly associated with the risk for invasive cancer than for ductal carcinoma in situ. Conclusions: Although a modest proportion of women changed BI-RADS density category, mammographic density changes modulated the risk of breast cancer and identified women at a differential risk. Using two longitudinal measures of BI-RADS density could help target women for risk-based screening strategies.

B reast density (BD) has gained significant attention in both the literature and the media during the past decade [1-5]. There currently is strong advocacy for women who undergo screening to be notified of their BD status, giving rise to legislation to allow shared decision-making regarding the management of women with dense breasts [2]. Heterogeneously dense and extremely dense breasts are prevalent in 31-43% of the general screening population [6]. High BD is associated with a four-to sixfold increased risk of breast cancer (BC) [7] and is related to other risk factors for BC [1, 8-10]. BD is also a determinant of mammographic sensitivity and interval BC [7, 11, 12]. Therefore, BD data can be used to improve BC risk prediction models [13-15] and to tailor screening to facilitate early detection of BC [16]. The fact that BD is modifiable [17] highlights the need both for women who undergo screening to be educated about BD and the risk of BC and for strategies for the man

Data on ethnic variations in breast density are limited and often not inclusive of underrepresented minorities. As breast density is associated with elevated breast cancer risk, investigating racial and ethnic difference may elucidate the observed differences in breast cancer risk among different populations. We reviewed breast density from initial screening of women from the Capital Breast Care Center and Georgetown University Hospital from 2010 to 2014. Patient demographics including race, age at screening, education, menopausal status, and body mass index were abstracted. We recorded the BI-RADS density categories: (1) "fatty," (2) "scattered fibroglandular densities," (3) "heterogeneously dense," and (4) "extremely dense." Multivariable unconditional logistic regression was used to identify predictors of breast density. Density categorization was recorded for 2146 women over the 5year period, comprising Blacks (n = 940), Hispanics (n = 893), and Whites (n = 314). Analysis of subject characteristics by breast density showed that high category is observed in younger, Hispanic, nulliparous, premenopausal, and nonobese women (t-test or chi-square test, P-values <.0001). Obese women are 70% less likely to have high density. Being Hispanic, premenopausal, and nonobese were predictive of high density on logistic regression. In this analysis of density distribution in a diverse sample, Hispanic women have the highest breast density, followed by Blacks and Whites. Unique in our findings is women who identify as Hispanic have the highest breast density and lower rates of obesity. Further investigation of the impact of obesity on breast density, especially in the understudied Hispanic group is needed.

Background-Breast density is an established, independent risk factor for breast cancer. Despite this, density has not been included in standard risk models or routinely disclosed to patients. However, this is changing in the face of legal mandates and advocacy efforts. Little information

Data on ethnic variations in breast density are limited and often not inclusive of underrepresented minorities. As breast density is associated with elevated breast cancer risk, investigating racial and ethnic difference may elucidate the observed differences in breast cancer risk among different populations. We reviewed breast density from initial screening of women from the Capital Breast Care Center and Georgetown University Hospital from 2010 to 2014. Patient demographics including race, age at screening, education, menopausal status, and body mass index were abstracted. We recorded the BI-RADS density categories: (1) &quot;fatty,&quot; (2) &quot;scattered fibroglandular densities,&quot; (3) &quot;heterogeneously dense,&quot; and (4) &quot;extremely dense.&quot; Multivariable unconditional logistic regression was used to identify predictors of breast density. Density categorization was recorded for 2146 women over the 5-year period, comprising Blacks (n = 940), Hispanics (n = 89...

A high mammographic breast density is considered to be a risk factor for breast cancer. However, only a small number of studies on the association between breast density and lifestyle have been performed. A cross-sectional study was performed using a survey with 29 questions on life history and lifestyle. The breast density on mammography was classified into 4 categories following the BI-RADS criteria. The subjects were 522 women with no medical history of breast cancer. The mean age was 53.3 years old. On multivariate analysis, only BMI was a significant factor determining breast density in premenopausal women (parameter estimate, -0.403; p value, 0.0005), and the density decreased as BMI rose. In postmenopausal women, BMI (parameter estimate, -0.196; p value, 0.0143) and number of deliveries (parameter estimate, -0.388; p value, 0.0186) were significant factors determining breast density;breast density decreased as BMI and number of deliveries increased. Only BMI and number of deliveries were identified as factors significantly influencing breast density. BMI was inversely correlated with breast density before and after menopause, whereas the influence of number of deliveries on breast density was significant only in postmenopausal women in their 50 and 60s.

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the sub...

Background: To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance. Methods: This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of <10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI. Results: Both mammographic breast density (VLD) (p < 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and high risk groups of the original NPI classification. The incorporation of both factors (VLD and HER2 status) into the NPI provided improved patient outcome stratification by decreasing the percentage of patients in the intermediate prognostic groups, moving a substantial percentage towards the low and high risk prognostic groups. Conclusions: Very low density (VLD) and HER2 positivity were prognostically significant factors independent of the NPI. Furthermore, the incorporation of VLD and HER2 to the NPI served to enhance its accuracy, thus offering a readily available and more accurate method for the evaluation of patient prognosis.

To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance. This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of &lt;10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI. Both mammographic breast density (VLD) (p &lt; 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and hi...

1) Background: As breast cancer studies suggest, a high percentage of breast density (PBD) may be related to breast cancer incidence. Although PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography evaluation is subjective. Therefore, new objective PBD measuring techniques are of interest. A case study analyzing the PBD of thirteen female participants using a bioimpedance-based method, the anomalies tracking circle (ATC), is described in this paper. (2) Methods: In the first stage, the breast bioimpedance of each participant was measured. Then, the participant breast density was determined by applying a mammogram just after the breast bioimpedance measurement stage. In the third stage, the ATC algorithm was applied to the measured bioimpedance data for each participant, and a results analysis was done. (3) Results: An ATC variation according to the breast density was observed from the obtained data, this allowed the use of classification techniques to determine the PBD. (4) Conclusions: The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain precise and objective results for evaluation of breast density and with that determine potential breast cancer risk.

To examine the relationships between breast cancer and both amount of fi broglandular tissue (FGT) and level of background parenchymal enhancement (BPE) at magnetic resonance (MR) imaging. Materials and Methods: A waiver of authorization was granted by the institutional review board for this retrospective HIPAA-compliant study. Among 1275 women who underwent breast MR imaging screening between December 2002 and February 2008, 39 breast carcinoma cases were identifi ed. Two comparisons were performed: In one comparison, two normal controls-those of the women with negative (benign) fi ndings at breast MR imaging-were matched to each breast cancer case on the basis of age and date of MR imaging. In the second comparison, one false-positive controlthat of a woman with suspicious but nonmalignant fi ndings at MR imaging-was similarly matched to each breast cancer case. Two readers independently rated the level of MR imaging-depicted BPE and the amount of MR imagingdepicted FGT by using a categorical scale: BPE was categorized as minimal, mild, moderate, or marked, and FGT was categorized as fatty, scattered, heterogeneously dense, or dense. Results: Compared with the odds ratio (OR) for a normal control, the OR for breast cancer increased signifi cantly with increasing BPE: The ORs for moderate or marked BPE versus minimal or mild BPE were 10.1 (95% confi dence interval [CI]: 2.9, 35.3; P , .001) and 3.3 (95% CI: 1.3, 8.3; P = .006) for readers 1 and 2, respectively. Similar odds were seen when the false-positive controls were compared with the breast cancer cases: The ORs for moderate or marked BPE versus minimal or mild BPE were 5.1 (95% CI: 1.4, 19.1; P = .005) and 3.7 (95% CI: 1.2, 11.2; P = .013) for readers 1 and 2, respectively. The breast cancer odds also increased with increasing FGT, but the BPE fi ndings remained signifi cant after adjustment for FGT. Conclusion: Increased BPE is strongly predictive of breast cancer odds.

Breast cancer screening by mammography is widely used. The diagnostic accuracy is limited, with a positive predictive value of 16%. Therefore, a stepwise investigation, with repeat mammography and confirmation by pathology, is usually proposed. Although this stepwise investigation intends to avoid overtreatment, the many false positives result in unnecessary fear and diagnostic surgery in many women. The false negatives are not known since these women have not been investigated. Given the estimated low risk of missing breast cancer and the slow growth, repeating a screening mammography every two years is sufficient. The false positive screening results increase with breast density, and breast density increases when hormone replacement therapy (HRT) is given. It, therefore, is suggested to use clinical judgment and stop HRT for 3 to 6 months before repeating the mammography instead of starting immediately a stepwise investigation in all women.

Background: Mammography is one of the imaging modalities that has been in existence for a long period of time in investigating the breast for pathologies. Its use in imaging the breast can be for the purpose of screening and diagnosis. Breast screening allows for early detection of breast cancer. Early detection of breast can save lives. Despite its important imaging modality for breast, mammography is not readily available in this part of the world. Our institution just acquired one recently. Objectives: Therefore, the aim of our study is to document the pattern of mammographic findings and to determine the prevalence of breast pathologies (benign and malignant) among symptomatic patients referred for mammography. Methodology: This descriptive cross-sectional study comprises 121 symptomatic women who presented for mammographic examination at the

Background Extensive mammographic density is associated with an increased risk of breast cancer and makes the detection of cancer by mammography difficult, but the influence of density on risk according to method of cancer detection is unknown. Methods We carried out three nested case-control studies in screened populations with 1112 matched case-control pairs. We examined the association of the measured percentage of density in the baseline mammogram with risk of breast cancer, according to method of cancer detection, time since the initiation of screening, and age. Results As compared with women with density in less than 10% of the mammogram, women with density in 75% or more had an increased risk of breast cancer (odds ratio, 4.7; 95% confidence interval [CI], 3.0 to 7.4), whether detected by screening (odds ratio, 3.5; 95% CI, 2.0 to 6.2) or less than 12 months after a negative screening examination (odds ratio, 17.8; 95% CI, 4.8 to 65.9). Increased risk of breast cancer, whether detected by screening or other means, persisted for at least 8 years after study entry and was greater in younger than in older women. For women younger than the median age of 56 years, 26% of all breast cancers and 50% of cancers detected less than 12 months after a negative screening test were attributable to density in 50% or more of the mammogram. Conclusions Extensive mammographic density is strongly associated with the risk of breast cancer detected by screening or between screening tests. A substantial fraction of breast cancers can be attributed to this risk factor.

This study aims to investigate the relationship between mammographic breast density and the surgical outcomes of breast cancer. PubMed, SCOPUS, Web of Science, Science Direct, and the Wiley Library were systematically searched for relevant literature. Rayyan QRCI was employed throughout this comprehensive process. Our results included ten studies with a total of 5017 women diagnosed with breast cancer. The follow-up duration ranged from 1 year to 15.1 years. Eight out of the twelve included studies reported that low mammographic breast density was significantly associated with no local recurrence, metachronous contralateral breast cancer, and fewer challenges in the preoperative and intraoperative phases. On the other hand, four studies reported that mammographic breast density is not linked to disease recurrence, survival, re-excision, or an incomplete clinical and pathological response. There is a significant association between low mammographic breast density and reduced challenges in the preoperative and intraoperative phases, as well as no local recurrence and fewer mastectomy cases. However, the link between mammographic breast density and disease recurrence, survival, re-excision, and incomplete clinical and pathological response is less clear, with some studies reporting no significant association. The findings suggest that mammographic breast density may play a role in certain aspects of breast cancer outcomes, but further research is needed to fully understand its impact.

Purpose Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Materials and Methods Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. Results For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m 2 .

In the absence of evidence-based screening recommendations for women with dense breasts, it is important to know if breast density notification increases women’s anxiety. This study describes psychological reactions and future screening intentions of women attending a public mammographic screening program in Western Australia. Two-thirds of notified women indicated that knowing their breast density made them feel informed, 21% described feeling anxious, and 23% confused. Of the notified women who reported anxiety, 96% intended to re-screen when due (compared to 91% of all notified women and 93% of controls; p = 0.007 and p &lt; 0.001, respectively). In summary, reported anxiety (following breast density notification) appears to increase women’s intentions for future screening, not the reverse.

Background: The incidence of breast cancer in Uganda has nearly tripled in three decades, the reasons not wholly understood. Country specific evidence is needed to better understand associated risk factors. Breast density is a strong predictor of breast cancer risk in Caucasian populations. The aim of this study was to establish whether increased breast density is a contributory risk factor for breast cancer amongst black African women. Methods: A case control study carried out at the National Referral and teaching Hospital, Mulago in Kampala Uganda over a 6 months period. All patients who presented for breast cancer screening and underwent a mammography breast examination during the study period had their breast densities measured using the BI-RADS classification were eligible. Those diagnosed with breast cancer during the period formed the cases and those who had a normal breast examination formed the controls. Mulago receives patients from all over the country but more so from th...

Breast physical phantoms are a basic tool for the assessment and verification of performance standards in daily clinical practice of x-ray breast imaging modalities. They are also invaluable in testing and evaluation of new x-ray breast modalities to be potentially established, e.g. breast computed tomography, dual-energy breast CT and phase-contrast mammography and tomography. Nowadays, there is a lack or there are only a limited number of breast physical phantoms available for this purpose.&#13; The aim of this study is to explore a range of 3D printing materials such as resins, PLA, ABS, Nylon etc., to determine their attenuation and refractive properties, and to finally compare them to the properties of the breast tissues: adipose, glandular and skin.&#13; To achieve this goal, step-wedge phantoms were computationally modeled and then manufactured using stereolithographic and fused-deposition modeling technologies. X-ray images of the phantoms were acquired, using monochromatic ...