Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study

A comparative analysis of risk factor associations with interval and screen-detected breast cancers: A large UK prospective study

The associations of certain factors, such as age and menopausal hormone therapy, with breast cancer risk are known to differ for interval and screen-detected cancers. However, the extent to which associations of other established breast cancer risk factors differ by mode of detection is unclear. We investigated associations of a wide range of risk factors using data from a large UK cohort with linkage to the National Health Service Breast Screening Programme, cancer registration, and other health records. We used Cox regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs) for associations between risk factors and breast cancer risk. A total of 9421 screen-detected and 5166 interval cancers were diagnosed in 517,555 women who were followed for an average of 9.72 years. We observed the following differences in risk factor associations by mode of detection: greater body mass index (BMI) was associated with a smaller increased risk of interval (RR per 5 unit increase 1.07, 95% CI 1.03-1.11) than screen-detected cancer (RR 1.27, 1.23-1.30); having a first-degree family history was associated with a greater increased risk of interval (RR 1.81, 1.68-1.95) than screen-detected cancer (RR 1.52, 1.43-1.61); and having had previous breast surgery was associated with a greater increased risk of interval (RR 1.85, 1.72-1.99) than screen-detected cancer (RR 1.34, 1.26-1.42). As these differences in associations were relatively unchanged after adjustment for tumour grade, and are in line with the effects of these factors on mammographic density, they are likely to reflect the effects of these risk factors on screening sensitivity.

Percentage mammographic density or absolute breast density for risk stratification in breast screening: Possible implications for socioeconomic health disparity

OBJECTIVES

Obesity levels and mortality from breast cancer are higher in more deprived areas of the UK, despite lower breast cancer incidence. Supplemental imaging for women with dense breasts has been proposed as a potential improvement to screening, but it is not clear how stratification by percentage mammographic density (%MD) would be reflected across socioeconomic groups. This study aims to clarify the associations between breast composition (dense and fatty tissue) and socioeconomic status in a multi-ethnic screening population.

METHODS

Demographic characteristics were collected for 62,913 participants in a UK breast screening programme (age, ethnicity, Index of Multiple Deprivation (IMD)). Automated mammographic measurements were derived: dense volume (DV), non-dense volume (NDV) and percent density (%MD). Correlations between deprivation and mammographic composition were examined before and after adjustment for age, ethnicity and NDV, using non-dense breast volume as a proxy for body mass index (BMI).

RESULTS

There was negligible correlation between deprivation and DV (r = 0.017; P < 0.001 in all cases), but NDV increased with increasing deprivation (Pearson r = 0.101). Correlations were weaker in the Asian and Chinese ethnic groups. %MD decreased with deprivation (r = -0.094) and adjustment for ethnicity did not alter the association between %MD and IMD (relative change, most to least deprived quintile IMD: 1.18; 95% confidence interval: 1.16, 1.21).

CONCLUSIONS

Deprivation-related differences in %MD in the screening population are largely explained by differences in breast fat volume (NDV) which reflects BMI. Women in more deprived areas, where obesity and breast cancer mortality rates are higher, have increased breast adiposity and may miss out on risk-adapted screening if stratification is based solely on %MD or BIRADS classification.

Severity and risk factors of interval breast cancer in Queensland, Australia: a population-based study

BACKGROUND

Interval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC.

METHODS

Telephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010-2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation.

RESULTS

Compared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9-4.3), high-grade (OR = 2.36, 1.9-2.9) and triple-negative cancers (OR = 2.55, 1.9-3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6-0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2-2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1-1.7), received hormone replacement therapy (2-10 years: OR = 1.33, 1.0-1.7; > 10 years: OR = 1.55, 1.1-2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2-2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2-2.0).

CONCLUSION

These results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.

Breast Cancer Mode of Detection in a Population-Based Cohort

OBJECTIVE

To evaluate how breast cancers come to clinical attention (mode of detection [MOD]) in a population-based cohort, determine the relative frequency of different MODs, and characterize patient and tumor characteristics associated with MOD.

PATIENTS AND METHODS

We used the Rochester Epidemiology Project to identify women ages 40 to 75 years with a first-time diagnosis of breast cancer from May 9, 2017, to May 9, 2019 (n=500) in a 9-county region in Minnesota. We conducted a retrospective medical record review to ascertain the relative frequency of MODs, evaluating differences between screening mammography vs all other MODs by breast density and cancer characteristics. Multiple logistic regression was conducted to examine the likelihood of MOD for breast density and stage of disease.

RESULTS

In our population-based cohort, 162 of 500 breast cancers (32.4%) were detected by MODs other than screening mammography, including 124 (24.8%) self-detected cancers. Compared with women with mammography-detected cancers, those with MODs other than screening mammography were more frequently younger than 50 years of age (P=.004) and had higher-grade tumors (P=.007), higher number of positive lymph nodes (P<.001), and larger tumor size (P<.001). Relative to women with mammography-detected cancers, those with MODs other than screening mammography were more likely to have dense breasts (odds ratio, 1.87; 95% CI, 1.20 to 2.92; P=.006) and advanced cancer at diagnosis (odds ratio, 3.58; 95% CI, 2.29 to 5.58; P<.001).

CONCLUSION

One-third of all breast cancers in this population were detected by MODs other than screening mammography. Increased likelihood of nonmammographic MODs was observed among women with dense breasts and advanced cancer.

Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10^-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.

Key steps for effective breast cancer prevention

Despite decades of laboratory, epidemiological and clinical research, breast cancer incidence continues to rise. Breast cancer remains the leading cancer-related cause of disease burden for women, affecting one in 20 globally and as many as one in eight in high-income countries. Reducing breast cancer incidence will likely require both a population-based approach of reducing exposure to modifiable risk factors and a precision-prevention approach of identifying women at increased risk and targeting them for specific interventions, such as risk-reducing medication. We already have the capacity to estimate an individual woman's breast cancer risk using validated risk assessment models, and the accuracy of these models is likely to continue to improve over time, particularly with inclusion of newer risk factors, such as polygenic risk and mammographic density. Evidence-based risk-reducing medications are cheap, widely available and recommended by professional health bodies; however, widespread implementation of these has proven challenging. The barriers to uptake of, and adherence to, current medications will need to be considered as we deepen our understanding of breast cancer initiation and begin developing and testing novel preventives.

Mammographic density and breast cancer screening

Mammographic density, which is determined by the relative amounts of fibroglandular tissue and fat in the breast, varies between women. Mammographic density is associated with a range of factors, including age and body mass index. The description of mammographic density has been transformed by the digitalization of mammography, which has allowed automation of the assessment of mammographic density, rather than using visual inspection by a radiologist. High mammographic density is important because it is associated with reduced sensitivity for the detection of breast cancer at the time of mammographic screening. High mammographic density is also associated with an elevated risk of developing breast cancer. Mammographic density appears to be on the causal pathway for some breast cancer risk factors, but not others. Mammographic density needs to be considered in the context of a woman's background risk of breast cancer. There is intense debate about the use of supplementary imaging for women with high mammographic density. Should supplementary imaging be used in women with high mammographic density and a clear mammogram? If so, what modalities of imaging should be used and in which women? Trials are underway to address the risks and benefits of supplementary imaging.

Staying abreast of imaging - Current status of breast cancer detection in high density breast

OBJECTIVES

The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions.

KEY FINDINGS

Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Breast density is well known to be a risk factor in patients with suspected/known breast neoplasia. Extensive research in the field of qualitative and quantitative analysis on different tissue characteristics of the breast has rapidly become the chief focus of breast imaging. A summary of the available guidelines and modalities of breast imaging, as well as new emerging techniques under study that can potentially provide an augmentation or even a replacement of those currently available.

CONCLUSION

Despite all the advances in technology and all the research directed towards breast cancer, detection of breast cancer in dense breasts remains a dilemma.

IMPLICATIONS FOR PRACTICE

It is of utmost importance to develop highly sensitive screening modalities for early detection of breast cancer.

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

Interval breast cancer risk associations with breast density, family history and breast tissue aging

Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.

Measuring height and weight as part of routine mammographic screening for breast cancer

Objectives

Body mass index is a strong predictor of post-menopausal breast cancer risk and (negatively) confounds the association between mammographic breast density and breast cancer risk; however, height and weight are not typically measured as part of routine mammographic screening. This study piloted voluntary height and weight measurement within the BreastScreen Western Australia (WA) programme, and assessed trial participation.

Methods

From February 2016 to January 2018, 204,429 women attending BreastScreen WA were invited to have their height and weight measured and recorded as part of their routine screening mammogram. Descriptive data analysis was used to assess pilot participation rates by available screening data.

Results

Of the 204,429 patients who attended BreastScreen WA during the pilot, 76.35% (156,072) agreed to have their height and weight measured. Pilot participation rates were significantly lower in those patients with disabilities (RR: 0.626; 95% CI: 0.600, 0.653), those who spoke a language other than English at home (RR: 0.876; 95% CI: 0.867, 0.885), and those who identified as Aboriginal and Torres Strait Islander (RR: 0.829; 95% CI: 0.807, 0.852). Pilot participation decreased over time from 88.9% in the first three months to 55.5% in the last month, due to lessening of support from BreastScreen staff.

Conclusion

Measuring height and weight at the time of routine mammographic screening is feasible, although logistical issues, particularly the added time/effort required of support staff, should be considered. BreastScreen WA has since decided to collect voluntary self-reported height and weight data as routine screening policy.

A review of the influence of mammographic density on breast cancer clinical and pathological phenotype

PURPOSE

It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context.

METHODS

With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival.

RESULTS AND CONCLUSIONS

There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.

Effect of younger age on survival outcomes in T1N0M0 breast cancer: A propensity score matching analysis

PURPOSE

We evaluated the effect of younger age on recurrence risk in Chinese women diagnosed with T1N0M0 breast cancer (BC), using propensity score matching (PSM) analysis.

METHODS

We included 365 women who were diagnosed with T1N0M0 BC between 2003 and 2016, and who received surgery at our center. They were classified as younger (≤40 years) and older (>40 years). We used PSM to balance clinicopathologic characteristics between the two age groups. Survival was analyzed by the Kaplan-Meier method, before and after PSM.

RESULTS

Over a median follow-up period of 79 months, 54 patients developed recurrences. Before PSM, younger patients had worse recurrence-free survival (RFS) than older patients. Significantly worse RFS was seen in younger patients with HER2⁺ BC compared with their older counterparts. Younger patients had higher rates of locoregional recurrence rather than metastasis, especially in the first 5 years after diagnosis. After PSM, the two age groups still significantly differed in 5-year RFS.

CONCLUSION

Among PSM pairs with T1N0M0 BC, with equal baselines and treatment conditions, we found that patients who presented at younger ages had worse outcomes, independently of other pathological features. Younger patients with BC may require more individualized therapy to improve their prognosis.

Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds

BACKGROUND

Case-control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers.

METHOD

We conducted a nested case-control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC).

RESULTS

For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85-2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07).

CONCLUSION

The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.

Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features

Background

We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.

Methods

Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.

Results

Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10⁻⁶). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10⁻⁵).

Conclusions

A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study

BACKGROUND

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.

OBJECTIVE

To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.

DESIGN

Case-control.

SETTING

San Francisco Mammography Registry and Mayo Clinic.

PARTICIPANTS

1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.

MEASUREMENTS

Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.

RESULTS

Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.

LIMITATION

Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.

CONCLUSION

Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors

BACKGROUND & AIMS

Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening.

METHODS

We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry.

RESULTS

In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk.

CONCLUSIONS

We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.

Mammographic density and risk of breast cancer by tumor characteristics: a case-control study

BACKGROUND

In a previous paper, we had assumed that the risk of screen-detected breast cancer mostly reflects inherent risk, and the risk of whether a breast cancer is interval versus screen-detected mostly reflects risk of masking. We found that inherent risk was predicted by body mass index (BMI) and dense area (DA) or percent dense area (PDA), but not by non-dense area (NDA). Masking, however, was best predicted by PDA but not BMI. In this study, we aimed to investigate if these associations vary by tumor characteristics and mode of detection.

METHODS

We conducted a case-control study nested within the Melbourne Collaborative Cohort Study of 244 screen-detected cases matched to 700 controls and 148 interval cases matched to 446 controls. DA, NDA and PDA were measured using the Cumulus software. Tumor characteristics included size, grade, lymph node involvement, and ER, PR, and HER2 status. Conditional and unconditional logistic regression were applied as appropriate to estimate the Odds per Adjusted Standard Deviation (OPERA) adjusted for age and BMI, allowing the association with BMI to be a function of age at diagnosis.

RESULTS

For screen-detected cancer, both DA and PDA were associated to an increased risk of tumors of large size (OPERA ~ 1.6) and positive lymph node involvement (OPERA ~ 1.8); no association was observed for BMI and NDA. For risk of interval versus screen-detected breast cancer, the association with risk for any of the three mammographic measures did not vary by tumor characteristics; an association was observed for BMI for positive lymph nodes (OPERA ~ 0.6). No associations were observed for tumor grade and ER, PR and HER2 status of tumor.

CONCLUSIONS

Both DA and PDA were predictors of inherent risk of larger breast tumors and positive nodal status, whereas for each of the three mammographic density measures the association with risk of masking did not vary by tumor characteristics. This might raise the hypothesis that the risk of breast tumours with poorer prognosis, such as larger and node positive tumours, is intrinsically associated with increased mammographic density and not through delay of diagnosis due to masking.

The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study

BACKGROUND

In the light of the breast density legislation in the USA, it is important to know a woman's breast cancer risk, but particularly her risk of a tumor that is not detected through mammographic screening (interval cancer). Therefore, we examined the associations of automatically measured volumetric breast density with screen-detected and interval cancer risk, separately.

METHODS

Volumetric breast measures were assessed automatically using Volpara version 1.5.0 (Matakina, New Zealand) for the first available digital mammography (DM) examination of 52,814 women (age 50 - 75 years) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. We excluded all screen-detected breast cancers diagnosed as a result of the first digital screening examination. During a median follow-up period of 4.2 (IQR 2.0-6.2) years, 523 women were diagnosed with breast cancer of which 299 were screen-detected and 224 were interval breast cancers. The associations between volumetric breast measures and breast cancer risk were determined using Cox proportional hazards analyses.

RESULTS

Percentage dense volume was found to be positively associated with both interval and screen-detected breast cancers (hazard ratio (HR) 8.37 (95% CI 4.34-16.17) and HR 1.39 (95% CI 0.82-2.36), respectively, for Volpara density grade category (VDG) 4 compared to VDG1 (p for heterogeneity < 0.001)). Dense volume (DV) was also found to be positively associated with both interval and screen-detected breast cancers (HR 4.92 (95% CI 2.98-8.12) and HR 2.30 (95% CI 1.39-3.80), respectively, for VDG-like category (C)4 compared to C1 (p for heterogeneity = 0.041)). The association between percentage dense volume categories and interval breast cancer risk (HR 8.37) was not significantly stronger than the association between absolute dense volume categories and interval breast cancer risk (HR 4.92).

CONCLUSIONS

Our results suggest that both absolute dense volume and percentage dense volume are strong markers of breast cancer risk, but that they are even stronger markers for predicting the occurrence of tumors that are not detected during mammography breast cancer screening.

Chemoprevention of Rat Mammary Carcinogenesis by Apiaceae Spices

Scientific evidence suggests that many herbs and spices have medicinal properties that alleviate symptoms or prevent disease. In this study, we examined the chemopreventive effects of the Apiaceae spices, anise, caraway, and celery seeds against 17β-estrogen (E2)-mediated mammary tumorigenesis in an ACI (August-Copenhagen Irish) rat model. Female ACI rats were given either control diet (AIN 93M) or diet supplemented with 7.5% (w/w) of anise, caraway, or celery seed powder. Two weeks later, one half of the animals in each group received subcutaneous silastic implants of E2. Diet intake and body weight were recorded weekly, and animals were euthanized after 3 and 12 weeks. E2-treatment showed significantly (2.1- and 3.4-fold) enhanced growth of pituitary gland at 3 and 12 weeks, respectively. All test spices significantly offset the pituitary growth by 12 weeks, except celery which was effective as early as three weeks. Immunohistochemical analysis for proliferative cell nuclear antigen (PCNA) in mammary tissues showed significant reduction in E2-mediated mammary cell proliferation. Test spices reduced the circulating levels of both E2 and prolactin at three weeks. This protection was more pronounced at 12 weeks, with celery eliciting the highest effect. RT-PCR and western blot analysis were performed to determine the potential molecular targets of the spices. Anise and caraway diets significantly offset estrogen-mediated overexpression of both cyclin D1 and estrogen receptor α (ERα). The effect of anise was modest. Likewise, expression of CYP1B1 and CYP1A1 was inhibited by all test spices. Based on short-term molecular markers, caraway was selected over other spices based on its enhanced effect on estrogen-associated pathway. Therefore, a tumor-end point study in ACI rats was conducted with dietary caraway. Tumor palpation from 12 weeks onwards revealed tumor latency of 29 days in caraway-treated animals compared with first tumor appearance at 92 days in control group. At the end of the study (25 weeks), the tumor incidence was 96% in the control group compared with only 70% in the caraway group. A significant reduction in tumor volume (661 ± 123 vs. 313 ± 81 mm³) and tumor multiplicity (4.2 ± 0.4 vs. 2.5 ± 0.5 tumors/animal) was also observed in the caraway group compared with the control group. Together, our data show dietary caraway can significantly delay and prevent the hormonal mammary tumorigenesis by modulating different cellular and molecular targets.

Breast density (BD) assessment with digital breast tomosynthesis (DBT): Agreement between Quantra™ and 5th edition BI-RADS®

PURPOSE

To assess the agreement between digital breast tomosynthesis (DBT) breast density (BD) assessment made using Quantra™ and fifth edition BI-RADS^®.

MATERIALS AND METHODS

This board approved study involved BD assessment of 234 women undergoing DBT investigation. BD estimation was performed from the raw DBT images using Quantra™ 3 (v.2.1.1, Hologic, Bedford MA). BI-RADS^® assessment was performed using prior digital mammograms displayed simultaneously with 2D images synthesized from DBT by three radiologists using the fifth edition BI-RADS^® (A, B, C, D). Kappa (к) was used to assess inter-reader agreement, agreement between Quantra™ and each reader, as well as the majority report of all readers. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of Quantra™ in reproducing the majority BI-RADS^® assessment. Data was then grouped into a two-category scale [almost entirely fatty and scattered fibroglandular tissue (A-B) versus heterogeneously dense and extremely dense (C-D)], and a further analysis performed.

RESULTS

Inter-reader agreement varied from fair [0.38 (95%CI: 0.30-0.46)] to substantial [0.68 (95%CI: 0.61-0.75)] on a four-category scale and substantial [0.70 (95%CI: 0.61-0.78)] to almost perfect [0.85 (95%CI: 0.78-0.92)] on a two-category scale. Using the majority report, the agreement between BI-RADS^® and Quantra™ was 0.68 (95%CI: 0.59-0.75) on a four-category scale and 0.86 (95%CI: 0.79-0.93) on a two-category scale. Quantra™ distinguished BI-RADS^® A-B from C-D with 97.1% sensitivity and 83.1% specificity.

CONCLUSION

Data demonstrate moderate to substantial agreement in BD assessment between fifth edition BI-RADS^® and Quantra™.

Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study

Background

Interval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability.

Methods

From a population-based cohort of invasive breast cancer cases in Stockholm-Gotland, Sweden, diagnosed from 2001 to 2008, we analyzed the contralateral mammogram at the preceding negative screening of 394 interval cancer cases and 1009 screen-detected cancers. We examined 32 different image features in digitized film mammograms, based on three alternative dense area identification methods, by a set of logistic regression models adjusted for percent density with interval cancer versus screen-detected cancer as the outcome. Features were forward-selected into a multiple logistic regression model adjusted for mammographic percent density, age, BMI and use of hormone replacement therapy. The associations of the identified features were assessed also in a sample from an independent cohort.

Results

Two image features, ‘skewness of the intensity gradient’ and ‘eccentricity’, were associated with the risk of interval compared with screen-detected cancer. For the first feature, the per-standard deviation odds ratios were 1.32 (95 % CI: 1.12 to 1.56) and 1.21 (95 % CI: 1.04 to 1.41) in the primary and validation cohort respectively. For the second feature, they were 1.20 (95 % CI: 1.04 to 1.39) and 1.17 (95%CI: 0.98 to 1.39) respectively. The first feature was associated with the tumor size at screen detection, while the second feature was associated with the tumor size at interval detection.

Conclusions

We identified two novel mammographic features in screening mammograms of the normal breast that differentiated between future interval cancers and screen-detected cancers. We present a starting point for further research into features beyond percent density that might be relevant for interval cancer, and suggest ways to use this information to improve screening.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0761-x) contains supplementary material, which is available to authorized users.