***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: Introduction The Predicting Risk of Cancer at Screening study in Manchester , UK , is a prospect i ve study of breast cancer risk estimation . It was design ed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model ( Breast Cancer Risk Assessment Tool ) or the Tyrer-Cuzick model ( International Breast Intervention Study model ) . Methods  Mammographic density  was measured at entry as a percentage visual assessment , adjusted for age and body mass index . Tyrer-Cuzick and Gail 10-year risks were based on a question naire completed contemporaneously . Breast cancers were identified at the entry screen or shortly thereafter . The contribution of density to risk models was assessed using odds ratios ( ORs ) with profile likelihood confidence intervals ( CIs ) and area under the receiver operating characteristic curve ( AUC ) . The calibration of predicted ORs was estimated as a percentage [ ( observed vs expected ( O/E ) ] from logistic regression . Results The analysis included 50,628 women aged 47–73 years who were recruited between October 2009 and September 2013 . Of these , 697 had breast cancer diagnosed after enrolment . Median follow-up was 3.2 years . Breast density [ interquartile range odds ratio ( IQR-OR ) 1.48 , 95 % CI 1.34–1.63 , AUC 0.59 ] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [ IQR-OR 1.36 ( 95 % CI 1.25–1.48 ) , O/E 60 % ( 95 % CI 44–74 ) , AUC 0.57 ] or the Gail model [ IQR-OR 1.22 ( 95 % CI 1.12–1.33 ) , O/E 46 % ( 95 % CI 26–65 % ) , AUC 0.55 ] . It continued to add information after allowing for Tyrer-Cuzick [ IQR-OR 1.47 ( 95 % CI 1.33–1.62 ) , combined AUC 0.61 ] or Gail [ IQR-OR 1.45 ( 95 % CI 1.32–1.60 ) , combined AUC 0.59 ] . Conclusions Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model PURPOSE To estimate the effects of a tablet-based , breast cancer risk education intervention for use in primary care setting s ( BreastCARE ) on patients ' breast cancer knowledge , risk perception and concern . METHODS From June 2011-August 2012 , we enrolled women from two clinics , aged 40 - 74 years with no personal breast cancer history , and r and omized them to the BreastCARE intervention group or to the control group .  All patients completed a baseline telephone survey and risk assessment ( via telephone for controls , via tablet computer in clinic waiting room prior to visit for intervention ) . All women were categorized as high or average risk based on the Referral Screening Tool , the Gail model or the Breast Cancer Surveillance Consortium model . Intervention patients and their physicians received an individualized risk report to discuss during the visit . All women completed a follow-up telephone survey 1 - 2 weeks after risk assessment . Post-test comparisons estimated differences at follow-up in breast cancer knowledge , risk perception and concern . RESULTS 580 intervention and 655 control women completed follow-up interviews . Mean age was 56 years ( SD = 9 ) . At follow-up , 73 % of controls and 71 % of intervention women correctly perceived their breast cancer risk and 22 % of controls and 24 % of intervention women were very concerned about breast cancer . Intervention patients had greater knowledge ( ≥75 % correct answers ) of breast cancer risk factors at follow-up ( 24 % vs. 16 % ; p = 0.002 ) . In multivariable analysis , there were no differences in correct risk perception or concern , but intervention patients had greater knowledge ( [ OR ] = 1.62 ; 95 % [ CI ] = 1.19 - 2.23 ) . CONCLUSIONS A simple , practical intervention involving physicians at the point of care can improve knowledge of breast cancer without increasing concern . TRIAL REGISTRATION Clinical Trials.gov identifier NCT01830933 Introduction Clinicians use different breast cancer risk models for patients considered at average and above-average risk , based largely on their family histories and genetic factors . We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT , also called Gail model , generally used for patients with average risk and IBIS , also called Tyrer Cuzick model , generally used for patients with above-average risk . Methods We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical setting s for 10-year absolute risk of breast cancer , using prospect i ve data from 1,857 women over a mean follow-up length of 8.1 years , of whom 83 developed cancer . This cohort spans the continuum of breast cancer risk , with some subjects at lower than average population risk . Therefore , the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women . For model calibration , we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow ( HL ) chi-squared statistic . For model discrimination , we computed the area under the receiver operator curve ( AUC ) and the case risk percentiles ( CRPs ) . Results The 10-year risks assigned by BCRAT and IBIS differed ( range of difference 0.001 to 79.5 ) . The mean BCRAT- and IBIS-assigned risks of 3.18 % and 5.49 % , respectively , were lower than the cohort 's 10-year cumulative probability of developing breast cancer ( 6.25 % ; 95 % confidence interval ( CI ) = 5.0 to 7.8 % ) . Agreement between assigned and observed risks was better for IBIS ( HL X42 = 7.2 , P value 0.13 ) than BCRAT ( HL X42 = 22.0 , P value < 0.001 ) . The IBIS model also showed better discrimination ( AUC = 69.5 % , CI = 63.8 % to 75.2 % ) than did the BCRAT model ( AUC = 63.2 % , CI = 57.6 % to 68.9 % ) . In almost all covariate-specific subgroups , BCRAT mean risks were significantly lower than the observed risks , while IBIS risks showed generally good agreement with observed risks , even in the subgroups of women considered at average risk ( for example , no family history of breast cancer , BRCA1/2 mutation negative ) . Conclusions Models developed using extended family history and genetic data , such as the IBIS model , also perform well in women considered at average risk ( for example , no family history of breast cancer , BRCA1/2 mutation negative ) . Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum Patients can benefit from accessible breast cancer risk information . The Gail model is a well-known means of providing risk information to patients and for guiding clinical decisions . Risk presentation often includes 5-year and lifetime percent chances for a woman to develop breast cancer . How do women perceive their risks after Gail model risk assessment ? This exploratory study used a r and omized clinical trial design to address this question among women not previously selected for breast cancer risk . Results suggest a brief  risk assessment intervention  changes quantitative and comparative risk perceptions and improves accuracy . This study improves our underst and ing of risk perceptions by evaluating an intervention in a population not previously selected for high-risk status and measuring perceptions in a variety of formats Purpose The Gail model is one of the most widely used tools to assess the risk of breast cancer . However , it is known to overestimate breast cancer risk for Asian women . Here , we vali date the Gail model and the Korean model using Korean data , and subsequently up date and revali date the Korean model using recent data . Methods We vali date d the  modified Gail model ( model 2 ) , Asian American Gail model , and a previous Korean model using screening patient data collected between January 1999 and July 2004 . The occurrence of breast cancer was confirmed by matching the resident registration number with data from the Korean Breast Cancer Registration Program . The expected-to-observed ( E/O ) ratio was used to vali date the reliability of the program , and receiver operating characteristics curve analysis was used to evaluate the program 's discriminatory power . There has been a rapid increase in the incidence of breast cancer in Korea , and we up date d and revali date d the Korean model using incidence and mortality rate data from recent years . Results Among 40,229 patients who were included in the validation , 161 patients were confirmed to have developed breast cancer within 5 years of screening . The E/O ratios and 95 % confidence intervals ( CI ) were 2.46 ( 2.10 - 2.87 ) for the modified Gail model and 1.29 ( 1.11 - 1.51 ) for the Asian American Gail model . The E/O ratio and 95 % CI for the Korean model was 0.50 ( 0.43 - 0.59 ) . For the up date d Korean model , the E/O ratio and 95 % CI were 0.85 ( 0.73 - 1.00 ) . In the discriminatory power , the area under curve and 95 % CI of the modified Gail model , Asian American Gail model , Korean model and up date d Korean model were 0.547 ( 0.500 - 0.594 ) , 0.543 ( 0.495 - 0.590 ) , 0.509 ( 0.463 - 0.556 ) , and 0.558 ( 0.511 - 0.605 ) , respectively . Conclusion The up date d Korean model shows a better performance than the other three models . It is hoped that this study can provide the basis for a clinical risk assessment program and a future prospect i ve study of breast cancer prevention Introduction The Gail model ( GM ) is a risk- assessment model used in individual estimation of the absolute risk of invasive breast cancer , and has been applied to both clinical counselling and breast cancer prevention studies . Although the GM has been vali date d in several Western studies , its applicability outside North America and Europe remains uncertain . The Singapore Breast Cancer Screening Project ( SBCSP ) is a nation-wide prospect i ve trial of screening mammography conducted between Oct 1994 and Feb 1997 , and is the only such trial conducted outside North America and Europe to date . With the long-term outcomes from this study , we sought to evaluate the performance of GM in prediction of individual breast cancer risk in a Asian developed country . Methods The study population consisted of 28,104 women aged 50 to 64 years who participated in the SBSCP and did not have breast cancer detected during screening . The national cancer registry was used to identify incident cases of breast cancer . To evaluate the performance of the GM , we compared the expected number of invasive breast cancer cases predicted by the model to the actual number of cases observed within 5-year and 10-year follow-up . Pearson 's Chi-square test was used to test the goodness of fit between the expected and observed cases of invasive breast cancers . Results The ratio of expected to observed number of invasive breast cancer cases within 5 years from screening was 2.51 ( 95 % confidence interval 2.14 - 2.96 ) . The GM over-estimated breast cancer risk across all age groups , with the discrepancy being highest among older women aged 60 - 64 years ( E/O = 3.53 , 95 % CI = 2.57 - 4.85 ) . The model also over-estimated risk for the upper 80 % of women with highest predicted risk . The overall E/O ratio for the 10-year predicted breast cancer risk was 1.85 ( 1.68 - 2.04 ) . Conclusions The GM over-predicts the risk of invasive breast cancer in the setting of a developed Asian country as demonstrated in a large prospect i ve trial , with the largest difference seen in older women aged between 60 and 64 years old . The reason for the discrepancy is likely to be multifactorial , including a truly lower prevalence of breast cancer , as well as lower mammographic screening prevalence locally To vali date an established breast cancer incidence model in an independent prospect i ve data set . After aligning time periods for follow-up , we restricted population s to comparable age ranges ( 47–74 years ) , and followed them for incident invasive breast cancer ( follow-up 1994–2008 , Nurses ’ Health Study [ NHS ] ; and 1995–2009 , California Teachers Study [ CTS ] ) . We identified 2026 cases during 540,617 person years of follow-up in NHS , and 1,400 cases during 288,111 person years in CTS . We fit the Rosner – Colditz log-incidence model and the Gail model using baseline data . We imputed future use of hormones based on type and prior duration of use and other covariates . We assessed performance using area under the curve ( AUC ) and calibration methods . Participants in the CTS had fewer children , were leaner , consumed more alcohol , and were more frequent users of postmenopausal hormones . Incidence rate ratios for breast cancer showed significantly higher breast cancer in the CTS ( IRR = 1.32 , 95 % CI 1.24–1.42 ) . Parameters for the log-incidence model were comparable across the two cohorts . Overall , the NHS model performed equally well when applied in the CTS . In the 
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Conclusion The results are not strong enough to develop breast cancer risk in the setting of Asian countries .