BackgroundClinical 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.PurposeTo compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.Materials and MethodsWomen were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.ResultsAmong 99 women, the automated mammographic density techniques were correlated with MRI measures with R2 values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.ConclusionAutomated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.

Structural changes in water molecules are related to physiological, anatomical and pathological properties of tissues. Near infrared (NIR) optical absorption methods are sensitive to water, however detailed characterization of water in thick tissues is difficult to achieve because subtle spectral shifts can be obscured by multiple light scattering. In the NIR, a water absorption peak is observed around 975nm. The precise NIR peak shape and position is highly sensitive to water molecular disposition. We introduce a Bound Water Index (BWI) that quantifies shifts observed in tissue water absorption spectra measured by broadband Diffuse Optical Spectroscopy (DOS). DOS quantitatively measures light absorption and scattering spectra and therefore reveals bound-water spectral shifts. BWI as a water state index was validated by comparing broadband DOS to Magnetic Resonance Spectroscopy, diffusion-weighted MRI and conductivity in bound water tissue phantoms. Non-invasive DOS measurements of malignant and normal breast tissues performed in 18 subjects showed a significantly higher fraction of free water in malignant tissues (p<0.0001) compared to normal tissues. BWI of breast cancer tissues inversely correlated with Nottingham-Bloom-Richardson histopathology scores. These results highlight broadband DOS sensitivity to molecular disposition of water, and demonstrate the potential of BWI as a non-invasive in-vivo index that correlates with tissue pathology.

A quantitative measure of three-dimensional breast density derived from noncontrast magnetic resonance imaging (MRI) was investigated in 35 women at high-risk for breast cancer. A semiautomatic segmentation tool was used to quantify the total volume of the breast and to separate volumes of fibroglandular and adipose tissue in noncontrast MRI data. The MRI density measure was defined as the ratio of breast fibroglandular volume over total volume of the breast. The overall correlation between MRI and mammographic density measures was R2=.67. However the MRI/mammography density correlation was higher in patients with lower breast density (R2=.73) than in patients with higher breast density (R2=.26). Women with mammographic density higher than 25% exhibited very different magnetic resonance density measures spread over a broad range of values. These results suggest that MRI may provide a volumetric measure more representative of breast composition than mammography, particularly in groups of women with dense breasts. Magnetic resonance imaging density could potentially be quantified and used for a better assessment of breast cancer risk in these populations.

These findings indicate that breast stroma tissue outside the incident tumor can be quantified using signal enhancement ratio analysis on dynamic contrast-enhanced MRI. Stromal signal enhancement ratio is a potential indicator for response to treatment and for overall outcome in patients with breast cancer; however, these results should be validated in a prospective study.

The study objective was to develop a segmentation technique to quantify breast tissue and total breast volume from magnetic resonance imaging (MRI) data to obtain a breast tissue index (BTI) related to breast density. Our goal is to quantify MR breast density to improve breast cancer risk assessment for certain high-risk populations for whom mammography is of limited usefulness due to high breast density. A semi-automatic 3D segmentation technique was implemented based on a fuzzy c-means technique (FCM) to segment fibroglandular tissue from fat in the breast images. After validation on a phantom, our FCM technique was first used to test the breast tissue measures reproducibility in two consecutive MR examinations of the same patients. The technique was then applied to measure the BTI on 10 high-risk patients. Results of BTI obtained with the semi-automated FCM method were compared with BTI results for the same patients using two other techniques, manual delineation and global threshold. BTI measures correlated well with mammographic densities (Pearson coefficients r = 0.78 using MR manual delineation, and r = 0.75 using MR FCM). The breast tissue index could therefore become a common measure for future studies of using noncontrast MRI data.

IntroductionBreast density is one of the strongest risk factors for breast cancer, but determinants of breast density in young women remain largely unknown.MethodsAssociations of height, adiposity and body fat distribution with percentage dense breast volume (%DBV) and absolute dense breast volume (ADBV) were evaluated in a cross-sectional study of 174 healthy women, 25 to 29 years old. Adiposity and body fat distribution were measured by anthropometry and dual-energy X-ray absorptiometry (DXA), while %DBV and ADBV were measured by magnetic resonance imaging. Associations were evaluated using linear mixed-effects models. All tests of statistical significance are two-sided.ResultsHeight was significantly positively associated with %DBV but not ADBV; for each standard deviation (SD) increase in height, %DBV increased by 18.7% in adjusted models. In contrast, all measures of adiposity and body fat distribution were significantly inversely associated with %DBV; a SD increase in body mass index (BMI), percentage fat mass, waist circumference and the android:gynoid fat mass ratio (A:G ratio) was each associated significantly with a 44.4 to 47.0% decrease in %DBV after adjustment for childhood BMI and other covariates. Although associations were weaker than for %DBV, all measures of adiposity and body fat distribution also were significantly inversely associated with ADBV before adjustment for childhood BMI. After adjustment for childhood BMI, however, only the DXA measures of percentage fat mass and A:G ratio remained significant; a SD increase in each was associated with a 13.8 to 19.6% decrease in ADBV. In mutually adjusted analysis, the percentage fat mass and the A:G ratio remained significantly inversely associated with %DBV, but only the A:G ratio was significantly associated with ADBV; a SD increase in the A:G ratio was associated with an 18.5% decrease in ADBV.ConclusionTotal adiposity and body fat distribution are independently inversely associated with %DBV, whereas in mutually adjusted analysis only body fat distribution (A:G ratio) remained significantly inversely associated with ADBV in young women. Research is needed to identify biological mechanisms underlying these associations.

Promising recent investigations have shown that breast malignancies exhibit restricted diffusion on diffusion-weighted imaging (DWI) and may be distinguished from normal tissue and benign lesions in the breast based on differences in apparent diffusion coefficient (ADC) values. In this study, we assessed the influence of intravoxel fat signal on breast diffusion measures by comparing ADC values obtained using a diffusion-weighted single shot fast spin echo sequence with and without fat suppression. The influence of breast density on ADC measures was also evaluated. ADC values were calculated for both tumor and normal fibroglandular tissue in a group of twenty-one women with diagnosed breast cancer. There were systematic underestimations of ADC for both tumor and normal breast tissue due to intravoxel contribution from fat signal on non-fat-suppressed DWI. This ADC underestimation was more pronounced for normal tissue values (mean difference = 40%) than for tumors (mean difference = 27%, p<0.001) and was worse in women with low breast tissue density versus those with extremely dense breasts (p<0.05 for both tumor and normal tissue). Tumor conspicuity measured by contrast-to-noise ratio was significantly higher on ADC maps created with fat suppression and was not significantly associated with breast density. In summary, robust fat suppression is important for accurate breast ADC measures and optimal lesion conspicuity on DWI.

Background: Adolescent diet is hypothesized to influence breast cancer risk. We evaluated the long-term effects of an intervention to lower fat intake among adolescent girls on biomarkers that are related to breast cancer risk in adults.Methods: A follow-up study was conducted on 230 girls who participated in the Dietary Intervention Study in Children (DISC), in which healthy, prepubertal, 8 to 10 year olds were randomly assigned to usual care or to a behavioral intervention that promoted a reduced fat diet. Participants were 25 to 29 years old at follow-up visits. All tests of statistical significance are two-sided.Results: In analyses that did not take account of diet at the time of the follow-up visit, the only statistically significant treatment group difference was higher bone mineral content in intervention group participants compared with usual care group participants; their mean bone mineral contents were 2,444 and 2,377 g, respectively. After adjustment for current diet, the intervention group also had statistically significantly higher bone mineral density and luteal phase serum estradiol concentrations. Serum progesterone concentrations and breast density did not differ by treatment group in unadjusted or adjusted analyses.Conclusions: Results do not support the hypothesis that consumption of a lower fat diet during adolescence reduces breast cancer risk via effects on subsequent serum estradiol and progesterone levels, breast density, or bone mineral density. It remains unclear, however, if the results are specific to the DISC intervention or are more broadly applicable.Impact: Modest reductions in fat intake during adolescence are unlikely to lower later breast cancer risk via long-term effects on the biomarkers measured. Cancer Epidemiol Biomarkers Prev; 19(6); 1545-56. ©2010 AACR.