Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.

Background: While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection.

Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy.

Background: Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer.

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density.

Using Digital Pathology to Understand Epithelial Characteristics of Benign Breast Disease among Women Undergoing Diagnostic Image-Guided Breast Biopsy.

Delayed terminal duct lobular unit (TDLU) involution is associated with elevated mammographic breast density (MD). Both are independent breast cancer risk factors among women with benign breast disease (BBD). Prior digital analyses of normal breast tissues revealed that epithelial nuclear density (END) and TDLU involution are inversely correlated.

Relationship of circulating insulin-like growth factor-I and binding proteins 1-7 with mammographic density among women undergoing image-guided diagnostic breast biopsy.

Background: Mammographic density (MD) is a strong breast cancer risk factor that reflects fibroglandular and adipose tissue composition, but its biologic underpinnings are poorly understood. Insulin-like growth factor binding proteins (IGFBPs) are markers that may be associated with MD given their hypothesized role in breast carcinogenesis. IGFBPs sequester IGF-I, limiting its bioavailability.

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

Background: To determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.

Methods: Full-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram.

Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.

Purpose: Women with radiographically dense or texturally complex breasts are at increased risk for interval cancer, defined as cancers diagnosed after a normal screening examination. The purpose of this study was to create masking measures and apply them to identify interval risk in a population of women who experienced either screen-detected or interval cancers after controlling for breast density.

Methods: We examined full-field digital screening mammograms acquired from 2006 to 2015.

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

Background: In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.

Objective: To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.

Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors.

Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer. Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen ( = 366) or AI ( = 403) and a sample of controls ( = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry.

Determinants of volumetric breast density in Chilean premenopausal women.

Purpose: High mammographic breast density (BD) is a strong risk factor of breast cancer; however, little is known in women under 40 years of age. Recently, dual-energy X-ray Absorptiometry (DXA) has been developed as a low-dose method to measure BD in young populations. Thus, our aims were to describe BD in relation to risk factors in Chilean women under 40 years old and to explore the equivalence of DXA to mammography for the measurement of BD.

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.

Background: Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.

Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis.

Background: Women with high levels of mammographic density (MD) have a four- to six-fold increased risk of developing breast cancer; however, most neither have a prevalent tumor nor will they develop one. Magnetic resonance imaging (MRI) studies suggest that background parenchymal enhancement, an indicator of vascularity, is related to increased breast cancer risk. Correlations of microvessel density (MVD) in tissue, MD and biopsy diagnosis have not been defined, and we investigated these relationships among 218 women referred for biopsy.

Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast: a cross-sectional study of women with benign breast disease.

Background: Terminal duct lobular units (TDLUs) are the primary structures from which breast cancers and their precursors arise. Decreased age-related TDLU involution and elevated mammographic density are both correlated and independently associated with increased breast cancer risk, suggesting that these characteristics of breast parenchyma might be linked to a common factor. Given data suggesting that increased circulating levels of insulin-like growth factors (IGFs) factors are related to reduced TDLU involution and increased mammographic density, we assessed these relationships using validated quantitative methods in a cross-sectional study of women with benign breast disease.

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities.

Elevated mammographic density (MD) is an established breast cancer risk factor. Reduced involution of terminal duct lobular units (TDLU), the histologic source of most breast cancers, has been associated with higher MD and breast cancer risk. We investigated relationships of TDLU involution with area and volumetric MD, measured throughout the breast and surrounding biopsy targets (perilesional).

The effect of change in body mass index on volumetric measures of mammographic density.

Background: Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.

Methods: We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013.

Hip Fractures Risk in Older Men and Women Associated With DXA-Derived Measures of Thigh Subcutaneous Fat Thickness, Cross-Sectional Muscle Area, and Muscle Density.

Mid-thigh cross-sectional muscle area (CSA), muscle attenuation, and greater trochanter soft tissue thickness have been shown to be independent risk factors of hip fracture. Our aim was to determine whether muscle and adipose tissue measures derived from dual-energy X-ray absorptiometry (DXA) scans would have a similar risk association as those measured using other imaging methods. Using a case-cohort study design, we identified 169 incident hip fracture cases over an average of 13.

Automated Volumetric Breast Density derived by Shape and Appearance Modeling.

The image shape and texture (appearance) estimation designed for facial recognition is a novel and promising approach for application in breast imaging. The purpose of this study was to apply a shape and appearance model to automatically estimate percent breast fibroglandular volume ) using digital mammograms. We built a shape and appearance model using 2000 full-field digital mammograms from the San Francisco Mammography Registry with known measured by single energy absorptiometry method.

Combining 3D optical imaging and dual energy absorptiometry to measure three compositional components.

We report on the design of the technique combining 3D optical imaging and dual-energy absorptiometry body scanning to estimate local body area compositions of three compartments. Dual-energy attenuation and body shape measures are used together to solve for the three compositional tissue thicknesses: water, lipid, and protein. We designed phantoms with tissue-like properties as our reference standards for calibration purposes.

Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification.

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy.

Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, "QIA alone," (2) the three-compartment breast (3CB) composition measure-derived from the dual-energy mammography-of water, lipid, and protein thickness were assessed, "3CB alone", and (3) information from QIA and 3CB was combined, "QIA + 3CB.

Agreement of mammographic measures of volumetric breast density to MRI.

Background: Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.

Purpose: To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.

Volume of mammographic density and risk of breast cancer.

Background: Assessing the volume of mammographic density might more accurately reflect the amount of breast volume at risk of malignant transformation and provide a stronger indication of risk of breast cancer than methods based on qualitative scores or dense breast area.

Methods: We prospectively collected mammograms for women undergoing screening mammography. We determined the diagnosis of subsequent invasive or ductal carcinoma in situ for 275 cases, selected 825 controls matched for age, ethnicity, and mammography system, and assessed three measures of breast density: percent dense area, fibroglandular volume, and percent fibroglandular volume.

Comparison of breast density measured by dual energy X-ray absorptiometry with mammographic density among adult women in Hawaii.

Background: While use of mammography is limited, due to concerns related to radiation exposure, dual energy X-ray absorptiometry (DXA), commonly available in medical care settings, is characterized by low radiation exposure.

Methods: In the current paper, we compared breast density measured by DXA with mammographic density in 101 adult women who had a screening mammogram during the last 2 years. DXA scans of both breasts were taken using a clinical DXA system calibrated to measure breast density.