Assessing women at high risk of breast cancer: a review of risk assessment models

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

BACKGROUND

The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.

MAIN TEXT

The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.

CONCLUSIONS

The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Malignancy Rate, Number Needed to Treat, and Positive Predictive Value for Breast MRI

Breast MRI is being used more frequently for advanced screening for breast cancer. Patients may be at increased risk, or are symptomatic, with nonsuspicious mammograms. There is little data regarding the likelihood of a recommendation for biopsy, or for detecting a malignancy, in this population. We intended to determine the malignancy rate, number needed to treat, and positive predictive value for patients receiving adjunctive MRI at our institution. A retrospective review of all breast MRIs from 2008 to 2010 was done. Patients with any prior diagnosis of breast cancer, or BRCA+ were excluded. There were 324 patients. Most common reasons for ordering the breast MRI included: abnormal test result 130 (44%), palpable mass 74 (23%), family history 58 (18%), breast pain 47 (15%), and nipple discharge 45 (14%). Breast Imaging-Reporting and Data System score (BIRADS) was 1 or 2 in 36 per cent, 4 or 5 in 18 per cent, 3 in 26 per cent, 0 in 10 per cent, and not given in 9 per cent. Biopsy was recommended in 77 (24%), with biopsy actually performed in 57 (18%). Of the eight cancers identified, four (1.2%) were ductal carcinoma in situ (DCIS) and four (1.2%) were invasive cancer, yielding a true-positive rate of 2.5 per cent. Number needed to treat was 40. Positive predictive value was 14 per cent with a false-positive rate of 86 per cent. In this group of generally higher risk women, typically prescreened with mammography, 1.2 per cent had an invasive cancer, and another 1.2 per cent had DCIS. Those who undergo biopsy are 6.1 times more likely to have benign pathology. The efficacy of adjunctive breast MRI could be improved through refinements in indication, test interpretation, or alternative screening strategies.

A new Suzuki synthesis of triphenylethylenes that inhibit aromatase and bind to estrogen receptors α and β

The design and synthesis of dual aromatase inhibitors/selective estrogen receptor modulators (AI/SERMs) is an attractive strategy for the discovery of new breast cancer therapeutic agents. Previous efforts led to the preparation of norendoxifen (4) derivatives with dual aromatase inhibitory activity and estrogen receptor binding activity. In the present study, some of the structural features of the potent AI letrozole were incorporated into the lead compound (norendoxifen) to afford a series of new dual AI/SERM agents based on a symmetrical diphenylmethylene substructure that eliminates the problem of E,Z isomerization encountered with norendoxifen-based AI/SERMs. Compound 12d had good aromatase inhibitory activity (IC₅₀=62.2nM) while also exhibiting good binding activity to both ER-α (EC₅₀=72.1nM) and ER-β (EC₅₀=70.8nM). In addition, a new synthesis was devised for the preparation of norendoxifen and its analogues through a bis-Suzuki coupling strategy.

The readability of online breast cancer risk assessment tools

Numerous breast cancer risk assessment tools that allow users to input personal risk information and obtain a personalized breast cancer risk estimate are available on the Internet. The goal of these tools is to increase screening awareness and identify modifiable health behaviors; however, the utility of this risk information is limited by the readability of the material. We undertook this study to assess the overall readability of breast cancer risk assessment tools and accompanying information, as well as to identify areas of suggested improvement. We searched for breast cancer risk assessment tools, using five search terms, on three search engines. All searches were performed on June 12, 2014. Sites that met inclusion criteria were then assessed for readability using the suitability assessment of materials (SAM) and the SMOG readability formula (July 1, 2014–January 31, 2015). The primary outcomes are the frequency distribution of overall SAM readability category (superior, adequate, or not suitable) and mean SMOG reading grade level. The search returned 42 sites were eligible for assessment, only 9 (21.4 %) of which achieved an overall SAM superior rating, and 27 (64.3 %) were deemed adequate. The average SMOG reading grade level was grade 12.1 (SD 1.6, range 9–15). The readability of breast cancer risk assessment tools and the sites that host them is an important barrier to risk communication. This study demonstrates that most breast cancer risk assessment tools are not accessible to individuals with limited health literacy skills. More importantly, this study identifies potential areas of improvement and has the potential to heighten a physician’s awareness of the Internet resources a patient might navigate in their quest for breast cancer risk information.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer

Background

We investigated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement kinetic variables quantified from normal breast parenchyma for association with presence of breast cancer, in a case-control study.

Methods

Under a Health Insurance Portability and Accountability Act compliant and Institutional Review Board-approved protocol, DCE-MRI scans of the contralateral breasts of 51 patients with cancer and 51 controls (matched by age and year of MRI) with biopsy-proven benign lesions were retrospectively analyzed. Applying fully automated computer algorithms on pre-contrast and multiple post-contrast MR sequences, two contrast enhancement kinetic variables, wash-in slope and signal enhancement ratio, were quantified from normal parenchyma of the contralateral breasts of both patients with cancer and controls. Conditional logistic regression was employed to assess association between these two measures and presence of breast cancer, with adjustment for other imaging factors including mammographic breast density and MRI background parenchymal enhancement (BPE). The area under the receiver operating characteristic curve (AUC) was used to assess the ability of the kinetic measures to distinguish patients with cancer from controls.

Results

When both kinetic measures were included in conditional logistic regression analysis, the odds ratio for breast cancer was 1.7 (95 % CI 1.1, 2.8; p = 0.017) for wash-in slope variance and 3.5 (95 % CI 1.2, 9.9; p = 0.019) for signal enhancement ratio volume, respectively. These odds ratios were similar on respective univariate analysis, and remained significant after adjustment for menopausal status, family history, and mammographic density. While percent BPE was associated with an odds ratio of 3.1 (95 % CI 1.2, 7.9; p = 0.018), in multivariable analysis of the three measures, percent BPE was non-significant (p = 0.897) and the two kinetics measures remained significant. For the differentiation of patients with cancer and controls, the unadjusted AUC was 0.71 using a combination of the two measures, which significantly (p = 0.005) outperformed either measure alone (AUC = 0.65 for wash-in slope variance and 0.63 for signal enhancement ratio volume).

Conclusions

Kinetic measures of wash-in slope and signal enhancement ratio quantified from normal parenchyma in DCE-MRI are jointly associated with presence of breast cancer, even after adjustment for mammographic density and BPE.

Dense Breasts: What Do Our Patients Need to Be Told and Why?

More than 50 % of states have state-mandated density notification for patients with heterogeneously or extremely dense breasts. Increased breast density carries a risk of masking a cancer and delaying diagnosis. Supplemental imaging is optional and often recommended for certain patients. There are no evidence-based consensus guidelines for screening patients with density as their only risk factor. Breast cancer risk assessment and breast cancer prevention strategies should be discussed with women with dense breasts.

Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development

The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series ("current") and the three most recent "prior" examinations were obtained. All "prior" examinations were interpreted as negative during the original clinical image reading, while in the "current" examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operating characteristic curves monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the "prior" (3 to 1) and "current" examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43, and 11.1 for the three "prior" (3 to 1) sets of examinations, respectively. This study demonstrated a positive association between the risk scores generated by a bilateral mammographic feature difference based risk model and an increasing trend of the near-term risk for having mammography-detected breast cancer.

The Genetics of Breast Cancer: What the Surgical Oncologist Needs to Know

This article summarizes the impact of germline predisposition to breast cancer on the surgical management of breast cancer and breast cancer risk. Surgical implications of germline predisposition to breast cancer are now more nuanced due to the application of increasingly more complicated next-generation sequencing-based tests. The rapid pace of change will continue to challenge paradigms for genetic cancer risk assessment, which can influence the medical and surgical management of breast cancer risk as well as strategies for screening and for risk reduction.

Bias Explains Most of the Parent-of-Origin Effect on Breast Cancer Risk in BRCA1/2 Mutation Carriers

BACKGROUND

Paternal transmission of a BRCA mutation has been reported to increase the risk of breast cancer in offspring more than when the mutation is maternally inherited. As this effect might be caused by referral bias, the aim of this study was to assess the parent-of-origin effect of the BRCA1/2 mutation on the breast cancer lifetime risk, when adjusted for referral bias.

METHODS

A Dutch national cohort including 1,314 proven BRCA1/2 mutation carriers and covering 54,752 person years. Data were collected by family cancer clinics, via questionnaires and from the national Dutch Cancer Registry. The parent-of-origin effect was assessed using Cox regression analyses, both unadjusted and adjusted for referral bias. Referral bias was operationalized by number of relatives with cancer and by personal cancer history.

RESULTS

The mutation was of paternal origin in 330 (42%, P < 0.001) BRCA1 and 222 (42%, P < 0.001) BRCA2 carriers. Paternal origin increased the risk of prevalent breast cancer for BRCA1 [HR, 1.54; 95% confidence interval (CI), 1.19-2.00] and BRCA2 carriers (HR, 1.40; 95% CI, 0.95-2.06). Adjusted for referral bias by several family history factors, these HRs ranged from 1.41 to 1.83 in BRCA1 carriers and 1.27 to 1.62 in BRCA2 carriers. Adjusted for referral bias by personal history, these HRs were 0.66 (95% CI, 0.25-1.71) and 1.14 (95% CI, 0.42-3.15), respectively.

CONCLUSION

A parent-of-origin effect is present after correction for referral bias by family history, but correction for the personal cancer history made the effect disappear.

IMPACT

There is no conclusive evidence regarding incorporating a BRCA1/2 parent-of-origin effect in breast cancer risk prediction models. Cancer Epidemiol Biomarkers Prev; 25(8); 1251-8. ©2016 AACR.

Affective forecasting and medication decision making in breast-cancer prevention

OBJECTIVE

Over 2 million American women at elevated risk for breast cancer are eligible to take chemoprevention medications such as tamoxifen and raloxifene, which can cut in half the risk of developing breast cancer, but which also have a number of side effects. Historically, very few at-risk women have opted to use chemoprevention medications. Affective forecasting theory suggests that people may avoid these medications if they expect taking them to increase their health-related stress.

METHOD

After receiving an individually tailored decision aid that provided personalized information about the risks and benefits of these medications, 661 women at elevated risk of breast cancer were asked to make 3 affective forecasts, predicting what their level of health-related stress would be if they took tamoxifen, raloxifene, or neither medication. They also completed measures of decisional preferences and intentions, and at a 3-month follow-up, reported on whether or not they had decided to use either medication.

RESULTS

On the affective forecasting items, very few women (<10%) expected the medications to reduce their health-related stress, relative to no medication at all. Participants with more negative affective forecasts about taking a chemoprevention medication expressed lower preferences and intentions for using the medications (Cohen's ds from 0.74 to 0.79) and were more likely to have opted against using medication at follow-up (OR range = 1.34-2.66).

CONCLUSION

These findings suggest that affective forecasting may explain avoidance of breast-cancer chemoprevention medications. They also highlight the need for more research aimed at integrating emotional content into decision aids. (PsycINFO Database Record

Breast cancer risk feedback to women in the UK NHS breast screening population

INTRODUCTION

There are widespread moves to develop risk-stratified approaches to population-based breast screening. The public needs to favour receiving breast cancer risk information, which ideally should produce no detrimental effects. This study investigates risk perception, the proportion wishing to know their 10-year risk and whether subsequent screening attendance is affected.

METHODS

Fifty thousand women attending the NHS Breast Screening Programme completed a risk assessment questionnaire. Ten-year breast cancer risks were estimated using a validated algorithm (Tyrer-Cuzick) adjusted for visually assessed mammographic density. Women at high risk (⩾8%) and low risk (<1%) were invited for face-to-face or telephone risk feedback and counselling.

RESULTS

Of those invited to receive risk feedback, more high-risk women, 500 out of 673 (74.3%), opted to receive a consultation than low-risk women, 106 out of 193 (54.9%) (P<0.001). Women at high risk were significantly more likely to perceive their risk as high (P<0.001) and to attend their subsequent mammogram (94.4%) compared with low-risk women (84.2%; P=0.04) and all attendees (84.3%; ⩽0.0001).

CONCLUSIONS

Population-based assessment of breast cancer risk is feasible. The majority of women wished to receive risk information. Perception of general population breast cancer risk is poor. There were no apparent adverse effects on screening attendance for high-risk women whose subsequent screening attendance was increased.

Using multiscale texture and density features for near-term breast cancer risk analysis

PURPOSE

To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk.

METHODS

The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index.

RESULTS

From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200).

CONCLUSIONS

The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

PURPOSE

Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region.

METHODS

Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance.

RESULTS

The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05).

CONCLUSIONS

The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older

BACKGROUND

The Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older.

METHODS

We used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided.

RESULTS

Seventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age.

CONCLUSIONS

BCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

BACKGROUND

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known.

METHODS

Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement.

RESULTS

The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score.

CONCLUSIONS

By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%.

IMPACT

Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.

Self-Reported Cancer Prevalence among Hispanics in the US: Results from the Hispanic Community Health Study/Study of Latinos

Cancer has surpassed heart disease as the leading cause of death among Hispanics in the U.S., yet data on cancer prevalence and risk factors in Hispanics in regard to ancestry remain scarce. This study sought to describe (a) the prevalence of cancer among Hispanics from four major U.S. metropolitan areas, (b) cancer prevalence across Hispanic ancestry, and (c) identify correlates of self-reported cancer prevalence. Participants were 16,415 individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), who self-identified as Cuban, Dominican, Mexican, Puerto Rican, Central or South American. All data were collected at a single time point during the HCHS/SOL baseline clinic visit. The overall self-reported prevalence rate of cancer for the population was 4%. The rates varied by Hispanic ancestry group, with individuals of Cuban and Puerto Rican ancestry reporting the highest cancer prevalence. For the entire population, older age (OR = 1.47, p < .001, 95% CI, 1.26-1.71) and having health insurance (OR = 1.93, p < .001, 95% CI, 1.42-2.62) were all significantly associated with greater prevalence, whereas male sex was associated with lower prevalence (OR = 0.56, p < .01, 95% CI, .40-.79). Associations between study covariates and cancer prevalence also varied by Hispanic ancestry. Findings underscore the importance of sociodemographic factors and health insurance in relation to cancer prevalence for Hispanics and highlight variations in cancer prevalence across Hispanic ancestry groups. Characterizing differences in cancer prevalence rates and their correlates is critical to the development and implementation of effective prevention strategies across distinct Hispanic ancestry groups.

Novel polymorphisms in caspase-8 are associated with breast cancer risk in the California Teachers Study

BACKGROUND

The ability of tamoxifen and raloxifene to decrease breast cancer risk varies among different breast cancer subtypes. It is important to determine one's subtype-specific breast cancer risk when considering chemoprevention. A number of single nucleotide polymorphisms (SNPs), including one in caspase-8 (CASP8), have been previously associated with risk of developing breast cancer. Because caspase-8 is an important protein involved in receptor-mediated apoptosis whose activity is affected by estrogen, we hypothesized that additional SNPs in CASP8 could be associated with breast cancer risk, perhaps in a subtype-specific manner.

METHODS

Twelve tagging SNPs of CASP8 were analyzed in a nested case control study (1,353 cases and 1,384 controls) of non-Hispanic white women participating in the California Teachers Study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each SNP using all, estrogen receptor (ER)-positive, ER-negative, human epidermal growth factor receptor 2 (HER2)-positive, and HER2-negative breast cancers as separate outcomes.

RESULTS

Several SNPs were associated with all, ER-positive, and HER2-positive breast cancers; however, after correcting for multiple comparisons (i.e., p < 0.0008), only rs2293554 was statistically significantly associated with HER2-positive breast cancer (OR = 1.98, 95% CI 1.34-2.92, uncorrected p = 0.0005).

CONCLUSIONS

While our results for CASP8 SNPs should be validated in other cohorts with subtype-specific information, we conclude that some SNPs in CASP8 are associated with subtype-specific breast cancer risk. This study contributes to our understanding of CASP8 SNPs and breast cancer risk by subtype.

Transitioning to routine breast cancer risk assessment and management in primary care: what can we learn from cardiovascular disease?

To capitalise on advances in breast cancer prevention, all women would need to have their breast cancer risk formally assessed. With ~85% of Australians attending primary care clinics at least once a year, primary care is an opportune location for formal breast cancer risk assessment and management. This study assessed the current practice and needs of primary care clinicians regarding assessment and management of breast cancer risk. Two facilitated focus group discussions were held with 17 primary care clinicians (12 GPs and 5 practice nurses (PNs)) as part of a larger needs assessment. Primary care clinicians viewed assessment and management of cardiovascular risk as an intrinsic, expected part of their role, often triggered by practice software prompts and facilitated by use of an online tool. Conversely, assessment of breast cancer risk was not routine and was generally patient- (not clinician-) initiated, and risk management (apart from routine screening) was considered outside the primary care domain. Clinicians suggested that routine assessment and management of breast cancer risk might be achieved if it were widely endorsed as within the remit of primary care and supported by an online risk-assessment and decision aid tool that was integrated into primary care software. This study identified several key issues that would need to be addressed to facilitate the transition to routine assessment and management of breast cancer risk in primary care, based largely on the model used for cardiovascular disease.

SNPs and breast cancer risk prediction for African American and Hispanic women

For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world’s population. We studied 7539 African American and 3363 Hispanic women from the Women’s Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19 % improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26 %, respectively.

Aromatase inhibitors for prevention of breast cancer in postmenopausal women: a narrative review

OBJECTIVE

The increasing incidence of breast cancer (BC) worldwide has resulted in widespread interest in primary prevention therapies. A number of large randomized trials have shown that selective estrogen receptor modulators can reduce the relative risk for BC by 30% to 40% in high-risk women. In early-stage BC, aromatase inhibitors (AIs) showed a 35% relative reduction in the risk of contralateral BCs compared with tamoxifen. In this narrative review, we discuss the role of AIs in the primary prevention of BC and novel research on combination hormone therapy-medical therapy for the primary prevention of BC.

METHODS

Using PubMed/Medline, we comprehensively searched for studies of BC primary prevention using AIs, including studies of novel methods of prevention using combination hormone therapy-BC prevention.

RESULTS

Two large multicenter, prospective, randomized, placebo-controlled trials have evaluated AIs--anastrozole (International Breast Cancer Intervention Study II) and exemestane (Mammary Prevention 3)--for BC risk reduction in women at increased risk for BC, which we summarize. We identified five studies (three completed and two ongoing) of combination AI-hormone therapy that are undergoing investigation for BC risk reduction.

CONCLUSIONS

AIs are effective at BC risk reduction, although long-term follow-up data are required to assess whether this risk reduction will result in reduced mortality. Combination hormone therapy-AI for BC risk reduction is experimental and warrants further investigation.

Impact of a decision aid on reducing uncertainty: pilot study of women in their 40s and screening mammography

BACKGROUND

In 2009 the United States Preventive Services Task Force updated its breast cancer screening guidelines to recommend that average-risk women obtain a screening mammogram every two years starting at age 50 instead of annually starting at age 40. Inconsistencies in data regarding the benefit versus risk of routine screening for women less than 50-years-of-age led to a second recommendation - that women in their forties engage in a shared decision making process with their provider to make an individualized choice about screening mammography that was right for them. In response, a web-based interactive mammography screening decision aid was developed and evaluated.

METHODS

The decision aid was developed using an agile, iterative process. It was further honed based on feedback from clinical and technical subject matter experts. A convenience sample of 51 age- and risk-appropriate women was recruited to pilot the aid. Pre-post decisional conflict and screening choice was assessed.

RESULTS

Women reported a significant reduction in overall decisional conflict after using the decision aid (Z = -5.3, p < 0.001). These participants also reported statistically significant reductions in each of the decisional conflict subscales: feeling uncertain (Z = -4.7, p < 0.001), feeling uninformed (Z = -5.2, p < 0.001), feeling unclear about values (Z = -5.0, p < 0.001), and feeling unsupported (Z = -4.0, p < 0.001). However, a woman's intention to obtain a screening mammogram in the next 1-2 years was not significantly changed (Wilcoxon signed-rank Z = -1.508, p = 0.132).

CONCLUSION

This mammography screening decision aid brings value to patient care not by impacting what a woman chooses but by lending clarity to why or how she chooses it.

A minimum spanning forest based classification method for dedicated breast CT images

PURPOSE

To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images.

METHODS

Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors' classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images.

RESULTS

Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively.

CONCLUSIONS

A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.

Risk Prediction Models for Colorectal Cancer: A Systematic Review

Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.

Menopausal Hormone Therapy: Current Considerations

Menopausal hormone therapy (MHT) is the most effective treatment for vasomotor and vaginal symptoms. Today, symptomatic women younger than 60 years of age or less than 10 years since onset of menopause yield the greatest benefit of MHT with the lowest risks when compared with older women remote from menopause. Careful assessment before initiating therapy includes severity of bothersome symptoms, treatment preferences, medical history, presence of contraindications to MHT, and personal risk of cardiovascular disease and breast cancer. Considerations of type of MHT, dosing, and route of administration, and recommendations regarding duration of therapy are discussed.

Breast cancer screening in the era of density notification legislation: summary of 2014 Massachusetts experience and suggestion of an evidence-based management algorithm by multi-disciplinary expert panel

Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review, the Cochrane review, National Comprehensive Cancer Network guidelines, American Cancer Society recommendations, and American College of Radiology appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman.

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography

INTRODUCTION

Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).

METHODS

Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.

RESULTS

All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).

CONCLUSIONS

Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Histopathological Features of Non-Neoplastic Breast Parenchyma Do Not Predict BRCA Mutation Status of Patients with Invasive Breast Cancer

BACKGROUND

Several studies have evaluated histologic features of non-neoplastic breast parenchyma in patients with BRCA1/2 mutations, but the results are conflicting. The limited data suggest a much higher prevalence of high-risk precursor lesions in BRCA carriers. Therefore, we designed this study to compare the clinicopathological characteristics of peritumoral benign breast tissue in patients with and without deleterious BRCA mutations.

METHODS

Women with breast cancer (BC) who were referred for genetic counseling and underwent BRCA genetic testing in 2010 and 2011 were included in the study.

RESULTS

Of the six benign histological features analyzed in this study, only stromal fibrosis grade 2/3 was found to be statistically different, with more BRCA noncarriers having stromal fibrosis grade 2/3 than BRCA1/2 carriers (P = 0.04).

CONCLUSION

There is no significant association between mutation risk and the presence of benign histologic features of peritumoral breast parenchyma.

Chemoprevention for Breast Cancer

Background

Many women at increased risk for breast cancer could benefit from preventive therapy. Preventive therapy options for breast cancer risk reduction have expanded in the last few years to include both selective receptor modulators (tamoxifen and raloxifene) and aromatase inhibitors (anastrozole and exemestane).

Methods

Risk factors that place women at high risk for breast cancer, as well as risk calculation models appropriate for the selection of candidates for preventive therapy, are presented, followed by a review of current guidelines for chemoprevention and results of chemoprevention trials.

Results

The modified Gail model or Breast Cancer Risk Assessment Tool is the most widely utilized risk assessment calculator to determine eligibility for chemoprevention. Women most likely to benefit from preventive therapy include those at high risk under the age of 50 years and those with atypical hyperplasia. Physician and patient barriers limit widespread acceptance and adherence to preventive therapy.

Conclusions

Published guidelines on chemoprevention for breast cancer have been updated to increase awareness and encourage discussion between patients and their physicians regarding evidence-based studies evaluating the benefits of preventive options for women at increased risk for breast cancer. However, even with increasing awareness and established benefits of preventive therapy, the uptake of chemoprevention has been low, with both physician and patient barriers identified. It is prudent that these barriers be overcome to enable high-risk women with a favorable risk-to-benefit ratio to be offered chemoprevention to reduce their likelihood of developing hormone receptor-positive breast cancer.

MRI screening for breast cancer in women at high risk; is the Australian breast MRI screening access program addressing the needs of women at high risk of breast cancer?

Breast magnetic resonance imaging (MRI) screening of women under 50 years old at high familial risk of breast cancer was given interim funding by Medicare in 2009 on the basis that a review would be undertaken. An updated literature review has been undertaken by the Medical Services Advisory Committee but there has been no assessment of the quality of the screening or other screening outcomes. This review examines the evidence basis of breast MRI screening and how this fits within an Australian context with the purpose of informing future modifications to the provision of Medicare-funded breast MRI screening in Australia. Issues discussed will include selection of high-risk women, the options for MRI screening frequency and measuring the outcomes of screening.

Can the breast screening appointment be used to provide risk assessment and prevention advice?

Breast cancer risk is continuing to increase across all societies with rates in countries with traditionally lower risks catching up with the higher rates in the Western world. Although cure rates from breast cancer have continued to improve such that absolute numbers of breast cancer deaths have dropped in many countries despite rising incidence, only some of this can be ascribed to screening with mammography, and debates over the true value of population-based screening continue. As such, enthusiasm for risk-stratified screening is gaining momentum. Guidelines in a number of countries already suggest more frequent screening in certain higher-risk (particularly, familial) groups, but this could be extended to assessing risks across the population. A number of studies have assessed breast cancer risk by using risk algorithms such as the Gail model, Tyrer-Cuzick, and BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm), but the real questions are when and where such an assessment should take place. Emerging evidence from the PROCAS (Predicting Risk Of Cancer At Screening) study is showing not only that it is feasible to undertake risk assessment at the population screening appointment but that this assessment could allow reduction of screening in lower-risk groups in many countries to 3-yearly screening by using mammographic density-adjusted breast cancer risk.

Impact of the new density reporting laws: radiologist perceptions and actual behavior

RATIONALE AND OBJECTIVES

To assess radiologists' perceptions of how the new Breast Density Notification Act (BDNA) of Pennsylvania would affect their breast density reporting and their actual reporting patterns after implementation.

MATERIALS AND METHODS

Under an institutional review board-approved protocol, we surveyed 21 radiologists about how they believe the new law affected their breast density reporting patterns and analyzed actual changes for 16 respondents before and after the law took effect. Three hundred consecutive reports were assessed for each radiologist before and after the effective date. The distributions of reported density Breast Imaging Reporting and Data System (BI-RADS) (1-4) were compared using a type III test in the context of an ordinal mixed model accounting for between-reader variability and adjusting for age (PROC GLIMMIX, SAS, version 9.3) using a two-sided .05 significance level.

RESULTS

Seventeen radiologists responded to the survey; however, one retired shortly after responding. Of the 16 respondents, 56% (nine of 16) did not favor the law, 13% (two of 16) were in favor, and 31% (five of 16) were neutral. The fraction perceived that after implementation, they rated more, equally, or less frequently breasts as scattered fibroglandular densities (BI-RADS 2) versus heterogeneously dense rating (BI-RADS 3) was 50% (eight of 16), 44% (seven of 16), and 6% (one of 16), respectively. In practice, 44% (seven of 16) performed differently than their survey answers. Fourteen of 16 radiologists increased the frequency of reported BI-RADS 2 scores after BDNA implementation with seven having statistically significant (P < .05) increases after adjusting for age differences.

CONCLUSIONS

Radiologists' reporting patterns changed, at least for a short duration, after the new density reporting law and for some of the radiologists in an unexpected way.

Survey on Addressing the Information and Support Needs of Jewish Women at Increased Risk for or Diagnosed with Breast Cancer: The Sharsheret Experience

Approximately 12% of women living in the United States will be diagnosed with breast cancer during their lifetimes. While all women face formidable challenges posed by the threat of living with or at increased risk for breast cancer, those of Ashkenazi Jewish descent face additional challenges owing to higher BRCA1/2 mutation prevalence in this population. Amidst calls for population-based screening for hereditary breast cancer risk, much can be learned from the experiences of Jewish women about their needs. The present study is a secondary analysis of psychoeducational program satisfaction and evaluation data previously collected by a community organization dedicated to serving women of all Jewish backgrounds facing, or at risk for, breast cancer. Among respondents (n = 347), over one-third were referred to the organization by family or friends, most often after a cancer crisis. Of the information and support resources offered, the greatest level of engagement occurred with the one-on-one peer support and health care symposia resources. Respondents endorsed high levels of satisfaction with the programs and services, and a strong desire to give back to the community. These data suggest that culturally-relevant information and support services for Jewish women could be scaled-up for larger dissemination to meet the anticipated needs in this special population.

A new approach to develop computer-aided detection schemes of digital mammograms

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman's age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Gene analysis techniques and susceptibility gene discovery in non-BRCA1/BRCA2 familial breast cancer

Breast cancer is the leading cause of cancer deaths in females worldwide occurring in both hereditary and sporadic forms. Women with inherited pathogenic mutations in the BRCA1 or BRCA2 genes have up to an 85% risk of developing breast cancer in their lifetimes. These patients are candidates for risk-reduction measures such as intensive radiological screening, prophylactic surgery or chemoprevention. However, only about 20% of familial breast cancer cases are attributed to mutations in BRCA1 and BRCA2, while a further 5-10% are attributed to mutations in other rare susceptibility genes such as TP53, STK11, PTEN, ATM and CHEK2. A multitude of genome wide association studies (GWAS) have been conducted confirming low-risk common variants associated with breast cancer in excess of 90 loci, which may contribute to a further 23% of the heritability. We currently find ourselves in "the next generation", with technologies offering deep sequencing at a fraction of the cost. Starting off primarily in a research setting, multi-gene panel testing is now utilized in the clinic to sequence multiple predisposing genes simultaneously (otherwise known as multi-gene panel testing). In this review, we focus on the hereditary breast cancer discoveries, techniques and the challenges we face in this complex disease, especially in the light of the vast amount of data we now have at hand. It has been 20 years since the first breast cancer susceptibility gene has been discovered and there has been substantial progress in unraveling the genetic component of the disease. However, hereditary breast cancer remains a challenging topic subject to common debate.

Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk

The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

Vitamin D receptor Cdx-2 polymorphism and premenopausal breast cancer risk in southern Pakistani patients

Background

Vitamin D3 is a secoster oid that exerts its effect by binding to its nuclear receptor called vitamin D receptor (VDR), inducing apoptosis and thereby inhibiting cell proliferation in cancer cells. The VDR receptor, located in the nucleus, is known to regulate the functions of over 200 genes. Various allelic forms of hVDR have been discovered that increase susceptibility to various cancers. The VDR-Cdx2 polymorphism, located in the promoter region of exon 1e in the VDR gene, influences the functional activity of the receptor, since the hVDR lacks consensus TATA and CAAT boxes. The current investigation examines the association between VDR-Cdx2 polymorphism and breast cancer in premenopausal females from Southern Pakistan.

Methods

We conducted a case control study on 264 subjects (103 cases and 161 controls) who were recruited from a tertiary hospital located in Karachi, Pakistan. Genomic DNA was extracted from peripheral blood using a commercial kit method, and the VDR-Cdx2 polymorphism was genotyped using tetraprimer amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) method. Pearson chi square test was used to assess the association between VDR-Cdx2 genotype and breast cancer while genotype distribution in controls was evaluated by Hardy-Weinberg equilibrium (HWE). Breast cancer risk was calculated using odds ratios and 95% confidence intervals.

Results

The genotype distribution in the control group was in HWE (p > 0.05) for the VDR-Cdx2 polymorphism. A non-significant association was observed between VDR cdx2 polymorphism and breast cancer, however the GG genotype was at risk (OR = 1.832, 95% CI = 0.695–4.828) of developing breast cancer.

Conclusion

The GG genotype of Cdx2-VDR gene polymorphism may increase the risk of developing breast cancer in young female patients in South Pakistan. Further investigations examining additional single nucleotide polymorphisms (SNPs) in VDR are required to assess their relationships with breast cancer.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography

Background

Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women.

Methods

In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk.

Results

The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively.

Conclusions

The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman’s risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1159-3) contains supplementary material, which is available to authorized users.

A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.

Impact of a randomized controlled educational trial to improve physician practice behaviors around screening for inherited breast cancer

BACKGROUND

Many primary care physicians (PCPs) are ill-equipped to provide screening and counseling for inherited breast cancer.

OBJECTIVE

To evaluate the outcomes of an interactive web-based genetics curriculum versus text curriculum for primary care physicians.

DESIGN

Randomized two-group design.

PARTICIPANTS

121 California and Pennsylvania community physicians.

INTERVENTION

Web-based interactive genetics curriculum, evaluated against a control group of physicians who studied genetics review articles. After education, physicians interacted with an announced standardized patient (SP) at risk for inherited breast cancer.

MAIN MEASURES

Transcripts of visit discussions were coded for presence or absence of 69 topics relevant to inherited breast cancer.

KEY RESULTS

Across all physicians, history-taking, discussions of test result implications, and exploration of ethical and legal issues were incomplete. Approximately half of physicians offered a genetic counseling referral (54.6%), and fewer (43.8%) recommended testing. Intervention physicians were more likely than controls to explore genetic counseling benefits (78.3% versus 60.7%, P = 0.048), encourage genetic counseling before testing (38.3% versus 21.3%, P = 0.048), ask about a family history of prostate cancer (25.0% versus 6.6%, P = 0.006), and report that a positive result indicated an increased risk of prostate cancer for male relatives (20.0% versus 1.6%, P = 0.001). Intervention-group physicians were less likely than controls to ask about Ashkenazi heritage (13.3% versus 34.4%, P = 0.01) or to reply that they would get tested when asked, "What would you do?" (33.3% versus 54.1%, P = 0.03).

CONCLUSIONS

Physicians infrequently performed key counseling behaviors, and this was true regardless of whether they had completed the web-based interactive training or read clinical reviews.

JNCI and cancer prevention

The Journal of the National Cancer Institute (JNCI), with its broad coverage of bench research, epidemiologic studies, and clinical trials, has a long history of publishing practice-changing studies in cancer prevention and public health. These include studies of tobacco cessation, chemoprevention, and nutrition. The landmark Breast Cancer Prevention Trial (BCPT)-the first large trial to prove efficacy of a preventive medication for a major malignancy-was published in the Journal, as were key ancillary papers to the BCPT. Even when JNCI was not the publication venue for the main trial outcomes, conceptual and design discussions leading to the trial as well as critical follow-up analyses based on trial data from the Prostate Cancer Prevention Trial (PCPT) and the Selenium and Vitamin E Chemoprevention Trial (SELECT) were published in the Journal. The Journal has also published important evidence on very charged topics, such as the purported link between abortion and breast cancer risk. In summary, JNCI has been at the forefront of numerous major publications related to cancer prevention.