***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background : Emerging evidence suggests positive associations between serum anti-Müllerian hormone ( AMH ) , a marker of ovarian function , and breast cancer risk . Body size at young ages may influence AMH levels , but few studies have examined this . Also , no studies have examined the relation of AMH levels with breast density , a strong predictor of breast cancer risk . Methods : We examined associations of early life body fatness , AMH concentrations , and breast density among 172 women in the Dietary Intervention Study in Children ( DISC ) . Height and weight were measured at baseline ( ages 8–10 ) and throughout adolescence . Serum AMH concentrations and breast density were assessed at ages 25–29 at the DISC 2006 Follow-up visit . We used linear mixed effects models to quantify associations of AMH ( dependent variable ) with quartiles of age-specific youth body mass index ( BMI ) Z-scores ( independent variable ) . We assessed cross-sectional associations of breast density ( dependent variable ) with AMH concentration ( independent variable ) . Results : Neither early life BMI nor current adult BMI was associated with AMH concentrations . There were no associations between AMH and percent or absolute dense breast volume . In contrast , women with higher  AMH concentrations  had significantly lower absolute nondense breast volume ( Ptrend < 0.01 ) . Conclusions : We found no evidence that current or early life BMI influences AMH concentrations in later life . Women with higher concentrations of AMH had similar percent and absolute dense breast volume , but lower nondense volume . Impact : These results suggest that AMH may be associated with lower absolute nondense breast volume ; however , future prospect i ve studies are needed to establish temporality . Cancer Epidemiol Biomarkers Prev ; 25(7 ) ; 1151–7 . © 2016 AACR Purpose To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort . To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and , in particular , to investigate the appropriateness of overlap measures ( e.g. , Dice and Jaccard coefficients ) as the primary determinant in algorithm selection . Methods Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children , a large cohort study with an MRI component . A semiautomated , bias‐corrected , fuzzy C‐means ( BC‐FCM ) method was combined with morphological operations to segment the overall breast volume from in‐phase Dixon images . The method makes use of novel , problem‐specific insights . The result ing segmentation mask was then applied to the corresponding Dixon water and fat images , which were combined to give Dixon MRI density values . Contemporaneously acquired T1‐ and T2‐weighted image data sets were analyzed using a novel and fully automated algorithm involving image filtering , l and mark identification , and explicit location of the pectoral muscle boundary . Within the region found , fat‐water discrimination was performed using an Expectation Maximization – Markov R and om Field technique , yielding a second independent estimate of MRI density . Results Images are presented for two individual women , demonstrating how the difficulty of the problem is highly subject‐specific . Dice and Jaccard coefficients comparing the semiautomated BC‐FCM method , operating on Dixon source data , with expert manual segmentation are presented . The corresponding results for the method based on T1‐ and T2‐weighted data are slightly lower in the individual cases shown , but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue . Conclusions Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm , rather than focus narrowly on a single metric such as the Dice coefficient Introduction Overweight and obesity in childhood and adolescence are associated with reduced breast  cancer  risk , independent of adult body mass index ( BMI ) . These associations may be mediated through breast density . Methods We prospect ively examined associations of early life body fatness with adult breast density measured by  MRI in 182 women in the Dietary Intervention Study in Children ( DISC ) who were ages 25–29 at follow-up . Height , weight , and other factors were measured at baseline ( ages 8–10 ) and annual clinic visits through adolescence . We used linear mixed-effects models to quantify associations of percent breast density and dense and non-dense breast volume at ages 25–29 with quartiles of age-specific youth body mass index ( BMI ) Z-scores , adjusting for clinic , treatment group , current adult BMI , and other well-established risk factors for breast cancer and predictors of breast density . Results We observed inverse associations between age-specific BMI Z-scores at all youth clinic visits and percent breast density , adjusting for current adult BMI and other covariates ( all p values < 0.01 ) . Women whose baseline BMI Z-scores ( at ages 8–10 years ) were in the top quartile had significantly lower adult breast density , after adjusting for current adult BMI and other covariates [ least squares mean ( LSM ) : 23.4 % ; 95 % confidence interval ( CI ) : 18.0 % , 28.8 % ] compared to those in the bottom quartile ( LSM : 31.8 % ; 95 % CI : 25.2 % , 38.4 % ) ( p trend < 0.01 ) . Significant inverse associations were also observed for absolute dense breast volume ( all p values < 0.01 ) , whereas there were no clear associations with non-dense breast volume . Conclusions These results support the hypothesis that body fatness during childhood and adolescence may play an important role in premenopausal breast density , independent of current BMI , and further suggest direct or indirect influences on absolute dense breast volume . Clinical Trials Registration NumberNCT00458588 ; April 9 , The purpose of this study was to investigate prospect ively whether the apparent diffusion coefficients ( ADCs ) of both breast cancer and normal fibrogl and ular tissue vary with the menstrual cycle and menopausal status . Institutional review board approval was obtained , and informed consent was obtained from each participant . Fifty-seven women ( 29 premenopausal , 28 postmenopausal ) with newly diagnosed breast cancer underwent diffusion-weighted imaging twice  ( interval 12–20 days ) before surgery .  Two radiologists independently measured ADC of breast cancer and normal contralateral breast tissue , and we quantified the differences according to the phases of menstrual cycle and menopausal status . With normal fibrogl and ular tissue , ADC was significantly lower in postmenopausal than in premenopausal women ( P = 0.035 ) . In premenopausal women , ADC did not differ significantly between proliferative and secretory phases in either breast cancer or normal fibrogl and ular tissue ( P = 0.969 and P = 0.519 , respectively ) . In postmenopausal women , no significant differences were found between ADCs measured at different time intervals in either breast cancer or normal fibrogl and ular tissue ( P = 0.948 and P = 0.961 , respectively ) . The within-subject variability of the ADC measurements was quantified using the coefficient of variation ( CV ) and was small : the mean CVs of tumor ADC were 2.90 % ( premenopausal ) and 3.43 % ( postmenopausal ) , and those of fibrogl and ular tissue ADC were 4.37 % ( premenopausal ) and 2.55 % ( postmenopausal ) . Both intra- and interobserver agreements were excellent for ADC measurements , with intraclass correlation coefficients in the range of 0.834–0.974 . In conclusion , the measured ADCs of breast cancer and normal fibrogl and ular tissue were not affected significantly by menstrual cycle , and the measurements were highly reproducible both within and between observers Women with high breast density ( BD ) have a 4- to 6-fold greater risk for breast cancer than women with low BD . We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging ( MRI ) data . These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models . We also found that the number of completed pregnancies , C-reactive protein , aspartate aminotransferase , and progesterone were more strongly associated with amounts of gl and ular tissue than adipose tissue , while fat body mass , alanine aminotransferase , and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue . Our results show that methods of breast imaging and modalities for estimating the amount of gl and ular tissue have no effects on the strength of these predictors of BD . Thus , the more convenient mathematical algorithm and the safer MRI protocol s may facilitate prospect i ve measurements of BD Introduction During adolescence the breasts undergo rapid growth and development under the influence of sex hormones . Although the hormonal etiology of breast cancer is hypothesized , it remains unknown whether adolescent sex hormones are associated with adult breast density , which is a strong risk factor for breast cancer . Methods Percentage of dense breast volume ( % DBV ) was measured in 2006 by magnetic resonance imaging in 177 women aged 25–29 years who had participated in the Dietary Intervention Study in Children from 1988 to 1997 . They had sex hormones and sex hormone-binding globulin ( SHBG ) measured in serum collected on one to five occasions between 8 and 17 years of age . Multivariable linear mixed-effect regression models were used to evaluate the associations of adolescent sex hormones and SHBG with % DBV . Results Dehydroepi and rosterone sulfate ( DHEAS ) and SHBG measured in premenarche serum sample s were significantly positively associated with % DBV ( all Ptrend ≤0.03 ) but not when measured in postmenarche sample s ( all Ptrend ≥0.42 ) . The multivariable geometric mean of % DBV across quartiles of premenarcheal DHEAS and SHBG increased from 16.7 to 22.1 % and from 14.1 to 24.3 % , respectively . Estrogens , progesterone , and rostenedione , and testosterone in pre- or postmenarche serum sample s were not associated with % DBV ( all Ptrend ≥0.16 ) . Conclusions Our results suggest that higher premenarcheal DHEAS and SHBG levels are associated with higher % DBV  in young women . Whether this association translates into an increased risk of breast cancer later in life is currently unknown . Clinical trials registration Clinical Trials.gov Identifier , NCT00458588 April 9 , 2007 ; NCT00000459 October 27 , BACKGROUND The amount of fibrogl and ular tissue ( FGT ) has been linked to breast cancer risk based on mammographic density studies . Currently , the qualitative assessment of FGT on mammogram ( MG ) and magnetic resonance imaging ( MRI ) is prone to intra and inter-observer variability . The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value . METHODS An IRB approved study was performed . Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were r and omly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014 . Blinded to the qualitative data , whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house , proprietary segmentation algorithm which combines the region-based active contours and a level set approach . FGT ( % ) was calculated by : [ segmented volume of FGT ( mm(3))/(segmented volume of whole breast ( mm(3 ) ) ] ×100 . Statistical correlation analysis was performed between quantified FGT ( % ) on MRI and qualitative assessment s of mammographic breast density and MRI FGT . RESULTS There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT ( r=0.809 , n=60 , P<0.001 ) and mammographic density assessment ( r=0.805 , n=60 , P<0.001 ) . There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment ( r=0.725 , n=60 , P<0.001 ) . The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT ( % ) of 4.61 % ( 95 % CI , 0 - 12.3 % ) , 8.74 % ( 7.3 - 10.2 % ) , 18.1 % ( 15.1 - 21.1 % ) , 37.4 % ( 29.5 - 45.3 % ) . CONCLUSIONS Quantitative measures of FGT ( % ) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessment s. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated st and ardized measurements with limited intra or inter-observer variability Background : Lack of association between fat intake and  breast cancer risk in cohort studies might be attributed to the disregard of temporal effects during adolescence when breasts develop and are particularly sensitive to stimuli . We prospect ively examined associations between  adolescent fat intakes  and breast density . Method : Among 177 women who participated in the Dietary Intervention Study in Children ,  dietary intakes at ages 10–18 years were assessed on five occasions by 24-hour recalls and averaged . We calculated geometric mean and 9 
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The review confirmed high levels of heterogeneity within the breast density studies , considered to be due mainly to the applications of MR breast-imaging protocol s and the use of breast density segmentation/measurement methods .