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. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. METHODS: Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. RESULTS: Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p < 0.001) and with local percent mammographic density volume at both the biopsy target (p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p < 0.05). CONCLUSIONS: Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

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. Methods: Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology. Results: The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60-0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Conclusion: Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

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. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

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. Accordingly, we examined associations of END, TDLU involution, and MD in BBD clinical biopsies. This study included digitized images of 262 representative image-guided hematoxylin and eosin-stained biopsies from 224 women diagnosed with BBD, enrolled within the cross-sectional BREAST-Stamp project that were visually assessed for TDLU involution (TDLU count/100 mm2, median TDLU span and median acini count per TDLU). A digital algorithm estimated nuclei count per unit epithelial area, or END. Single X-ray absorptiometry of prebiopsy ipsilateral craniocaudal digital mammograms measured global and localized MD surrounding the biopsy region. Adjusted ordinal logistic regression models assessed relationships between tertiles of TDLU and END measures. Analysis of covariance examined mean differences in MD across END tertiles. TDLU measures were positively associated with increasing END tertiles [TDLU count/100 mm2, ORT3vsT1: 3.42, 95% confidence interval (CI), 1.87-6.28; acini count/TDLUT3vsT1, OR: 2.40, 95% CI, 1.39-4.15]. END was significantly associated with localized, but not, global MD. Relationships were most apparent among patients with nonproliferative BBD. These findings suggest that quantitative END reflects different but complementary information to the histologic information captured by visual TDLU and radiologic MD measures and merits continued evaluation in assessing cellularity of breast parenchyma to understand the etiology of BBD.

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. Prior studies have found positive associations between circulating IGF-I and the IGF-I:IGFBP-3 ratio and breast cancer risk. We evaluated the associations of IGF-I, IGFBP-3, and six other IGFBPs with MD. METHODS: Serum IGF measures were quantified in 296 women, ages 40-65, undergoing diagnostic image-guided breast biopsy. Volumetric density measures (MD-V) were assessed in pre-biopsy digital mammograms using single X-ray absorptiometry. Area density measures (MD-A) were estimated by computer-assisted thresholding software. Age, body mass index (BMI), and BMI2-adjusted linear regression models were used to examine associations of serum IGF measures with MD. Effect modification by BMI was also assessed. RESULTS: IGF-I and IGFBP-3 were not strongly associated with MD after BMI adjustment. In multivariable analyses among premenopausal women, IGFBP-2 was positively associated with both percent MD-V (ß = 1.49, p value = 0.02) and MD-A (ß = 1.55, p value = 0.05). Among postmenopausal women, positive relationships between IGFBP-2 and percent MD-V (ß = 2.04, p = 0.003) were observed; the positive associations between IGFBP-2 and percent MD-V were stronger among lean women (BMI < 25 kg/m2) (ß = 5.32, p = 0.0002; p interaction = 0.0003). CONCLUSIONS: In this comprehensive study of IGFBPs and MD, we observed a novel positive association between IGFBP-2 and MD, particularly among women with lower BMI. In concert with in vitro studies suggesting a dual role of IGFBP-2 on breast tissue, promoting cell proliferation as well as inhibiting tumorigenesis, our findings suggest that further studies assessing the role of IGFBP-2 in breast tissue composition, in addition to IGF-1 and IGFBP-3, are warranted.

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. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables. RESULTS: Pre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information. CONCLUSIONS: Pre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection.

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. Examinations associated with 182 interval cancers were matched to 173 screen-detected cancers on age, race, exam date and time since last imaging examination. Local Image Quality Factor (IQF) values were calculated and used to create IQF maps that represented mammographic masking. We used various statistics to define global masking measures of these maps. Association of these masking measures with interval cancer vs screen-detected cancer was estimated using conditional logistic regression in a univariate and adjusted model for Breast Imaging-Reporting and Data System (BI-RADS) density. Receiver operator curves were calculated in each case to compare specificity vs sensitivity, and area under those curves were generated. Proportion of screen-detected cancer was estimated for stratifications of IQF features. RESULTS: Several masking features showed significant association with interval compared to screen-detected cancers after adjusting for BI-RADS density (up to P = 2.52E-6), and the 10th percentile of the IQF value (P = 1.72E-3) showed the strongest improvement in the area under the receiver operator curve, increasing from 0.65 using only BI-RADS density to 0.69. The highest masking group had a 32% proportion of screen-detected cancers while the low masking group had a 69% proportion. CONCLUSIONS: We conclude that computer vision methods using model observers may improve quantifying the probability of breast cancer detection beyond using breast density alone.

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. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

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. Design: Case-control. Setting: San Francisco Mammography Registry and Mayo Clinic. Participants: 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Measurements: Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Results: Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Limitation: Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method. Conclusion: Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. Primary Funding Source: National Cancer Institute.

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

Background: 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.Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm3 compared with 0.28 to 0.63 cm3 in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930-7. ©2017 AACR.

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. METHODS: We selected 192 premenopausal Chilean female participants of the DERCAM study for whom we have anthropometric, sociodemographic, and gyneco-obstetric data. The subjects received both digital mammograms (Hologic) and breast DXA scans (GE iDXA). Mammographic BD was estimated using a fully automated commercial method (VOLPARA®) and BI-RADS. Breast DXA scans were performed using a standardized protocol and the % fibroglandular volume (%FGV) was estimated considering a two-compartment model of adipose and fibroglandular tissue. RESULTS: The mean age was 37 years (SD = 6.5) and 31.6% of the subjects were obese. The median %FGV and absolute FGV (AFGV) measured by DXA were 9% and 198.1 cm3 and for VOLPARA®, 8.6% and 58.0 cm3, respectively. The precision for %FGV after reposition was 2.8%. The correlation coefficients for %FGV, AFGV, and breast volume between DXA and mammography were over 0.7. Age and body mass index (BMI) were inversely associated with %FGV, and BMI was positively related to AFGV as estimated with DXA or mammography. We did not observe an association with gyneco-obstetric characteristics, education, and %FGV and AFGV; smoking was only associated with AFGV as measured by VOLPARA®. CONCLUSIONS: DXA is an alternative method to measure volumetric BD; thus, it could be used to continuously monitor BD in adult women in follow-up studies or to assess BD in young women.

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. METHODS: This study combines five case-control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study. RESULTS: Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH_10 [corrected] and FD_TH_15) [corrected] were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH_60 to FD_TH_85) [corrected] were associated with a decreased risk. Increasing the FD_TH_75 [corrected] and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased [corrected] risk of breast cancer. For example, 1 standard deviation increase of FD_TH_75 [corrected] was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79-0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar. CONCLUSION: Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.

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. METHODS: MVD was determined by counting CD31-positive vessels in whole sections of breast biopsies in three representative areas; average MVD was transformed to approximate normality. Using digital mammograms, we quantified MD volume with single X-ray absorptiometry. We used linear regression to evaluate associations between MVD and MD adjusted for age and body mass index (BMI) overall, and stratified by biopsy diagnosis: cases (in situ or invasive cancer, n = 44) versus non-cases (non-proliferative or proliferative benign breast disease, n = 174). Logistic regression adjusted for age, BMI, and MD was used to calculate odds ratios (ORs) and 95 % confidence intervals (CIs) for associations between MVD and biopsy diagnosis. We also assessed whether the MVD-breast cancer association varied by MD. RESULTS: MVD and MD were not consistently associated. Higher MVD was significantly associated with higher odds of in situ/invasive disease (ORAdjusted = 1.69, 95 % CI = 1.17-2.44). MVD-breast cancer associations were strongest among women with greater non-dense volume. CONCLUSIONS: Increased MVD in tissues is associated with breast cancer, independently of MD, consistent with MRI findings suggestive of its possible value as a radiological cancer biomarker.

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. METHODS: Serum IGF-I, IGFBP-3 and IGF-I:IGFBP-3 molar ratios were measured in 228 women, ages 40-64, who underwent diagnostic breast biopsies yielding benign diagnoses at University of Vermont affiliated centers. Biopsies were assessed for three separate measures inversely related to TDLU involution: numbers of TDLUs per unit of tissue area ("TDLU count"), median TDLU diameter ("TDLU span"), and number of acini per TDLU ("acini count"). Regression models, stratified by menopausal status and adjusted for potential confounders, were used to assess the associations of TDLU count, median TDLU span and median acini count per TDLU with tertiles of circulating IGFs. Given that mammographic density is associated with both IGF levels and breast cancer risk, we also stratified these associations by mammographic density. RESULTS: Higher IGF-I levels among postmenopausal women and an elevated IGF-I:IGFBP-3 ratio among all women were associated with higher TDLU counts, a marker of decreased lobular involution (P-trend = 0.009 and <0.0001, respectively); these associations were strongest among women with elevated mammographic density (P-interaction <0.01). Circulating IGF levels were not significantly associated with TDLU span or acini count per TDLU. CONCLUSIONS: These results suggest that elevated IGF levels may define a sub-group of women with high mammographic density and limited TDLU involution, two markers that have been related to increased breast cancer risk. If confirmed in prospective studies with cancer endpoints, these data may suggest that evaluation of IGF signaling and its downstream effects may have value for risk prediction and suggest strategies for breast cancer chemoprevention through inhibition of the IGF system.

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 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

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). Three measures inversely related to TDLU involution (TDLU count/mm(2), median TDLU span, median acini count/TDLU) assessed in benign diagnostic biopsies from 348 women, ages 40-65, were related to MD area (quantified with thresholding software) and volume (assessed with a density phantom) by analysis of covariance, stratified by menopausal status and adjusted for confounders. Among premenopausal women, TDLU count was directly associated with percent perilesional MD (P trend = 0.03), but not with absolute dense area/volume. Greater TDLU span was associated with elevated percent dense area/volume (P trend<0.05) and absolute perilesional MD (P = 0.003). Acini count was directly associated with absolute perilesional MD (P = 0.02). Greater TDLU involution (all metrics) was associated with increased nondense area/volume (P trend ≤ 0.04). Among postmenopausal women, TDLU measures were not significantly associated with MD. Among premenopausal women, reduced TDLU involution was associated with higher area and volumetric MD, particularly in perilesional parenchyma. Data indicating that TDLU involution and MD are correlated markers of breast cancer risk suggest that associations of MD with breast cancer may partly reflect amounts of at-risk epithelium. If confirmed, these results could suggest a prevention paradigm based on enhancing TDLU involution and monitoring efficacy by assessing MD reduction.

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. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. RESULTS: In cross-sectional analysis, BMI was positively associated with DV [ß, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (ß, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (ß, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (ß, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. CONCLUSION: Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. IMPACT: Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.

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 13.5 years among participants from the Health ABC Study, a prospective study of 3075 individuals initially aged 70 to 79 years. We modeled the thigh 3D geometry and compared DXA and computed tomography (CT) measures. DXA-derived thigh CSA, muscle attenuation, and subcutaneous fat thickness were found to be highly correlated to their CT counterparts (Pearson's r = 0.82, 0.45, and 0.91, respectively; p < 0.05). The fracture risk of men and women were calculated separately. We found that decreased subcutaneous fat, CT thigh muscle attenuation, and appendicular lean mass by height squared (ALM/Ht(2)) were associated with fracture risk in men; hazard ratios (HR) = 1.44 (1.02, 2.02), 1.40 (1.05, 1.85), and 0.58 (0.36, 0.91), respectively, after adjusting for age, race, clinical site, body mass index (BMI), chronic disease, hip bone mineral density (BMD), self-reported health, alcohol use, smoking status, education, physical activity, and cognitive function. In a similar model for women, only decreases in subcutaneous fat and DXA CSA were associated with hip fracture risk; HR = 1.39 (1.07, 1.82) and 0.78 (0.62, 0.97), respectively. Men with a high ALM/Ht(2) and low subcutaneous fat thickness had greater than 8 times higher risk for hip fracture compared with those with low ALM/Ht(2) and high subcutaneous fat. In women, ALM/Ht(2) did not improve the model when subcutaneous fat was included. We conclude that the DXA-derived subcutaneous fat thickness is a strong marker for hip fracture risk in both men and women, especially in men with high ALM/Ht(2).

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. The calibration was created by fitting phantom values using non-linear regression of quadratic and truncated polynomials. Dual-energy measurements were performed on tissue-mimicking phantoms using a bone densitometer unit. The phantoms were made of materials shown to have similar x-ray attenuation properties of the biological compositional compartments. The components for the solid phantom were tested and their high energy/low energy attenuation ratios are in good correspondent to water, lipid, and protein for the densitometer x-ray region. The three-dimensional body shape was reconstructed from the depth maps generated by Microsoft Kinect for Windows. We used open-source Point Cloud Library and freeware software to produce dense point clouds. Accuracy and precision of compositional and thickness measures were calculated. The error contributions due to two modalities were estimated. The preliminary phantom composition and shape measurements are found to demonstrate the feasibility of the method proposed.