An association of boswellia, betaine and myo-inositol (Eumastós) in the treatment of mammographic breast density. A randomized, double-blind study

Mammographic breast density is a recognized risk factor for breast cancer. The causes that lead to the proliferation of the glandular breast tissue and, therefore, to an increase of breast density are still unclear. However, a treatment strategy to reduce the mammary density may bring about very relevant clinical outcomes in breast cancer prevention. Myo-inositol is a six-fold alcohol of cyclohexane, has already been proved to modulate different pathways: inflammatory, metabolic, oxidative and endocrine processes, in a wide array of human diseases, including cancer and the genesis of mammary gland and breast diseases, like fibrosis, as well as metabolic and endocrine cues. Similarly, boswellic acid and betaine (three-methyl glycine) both inhibit inflammation and exert protective effects on breast physiology. Based on this scientific background, we hypothesized that a combination including, boswellic acid, betaine and myo-inositol would be able to reduce breast density working on different pathways.OBJECTIVE: Mammographic breast density is a recognized risk factor for breast cancer. The causes that lead to the proliferation of the glandular breast tissue and, therefore, to an increase of breast density are still unclear. However, a treatment strategy to reduce the mammary density may bring about very relevant clinical outcomes in breast cancer prevention. Myo-inositol is a six-fold alcohol of cyclohexane, has already been proved to modulate different pathways: inflammatory, metabolic, oxidative and endocrine processes, in a wide array of human diseases, including cancer and the genesis of mammary gland and breast diseases, like fibrosis, as well as metabolic and endocrine cues. Similarly, boswellic acid and betaine (threemethyl glycine) both inhibit inflammation and exert protective effects on breast physiology. Based on this scientific background, we hypothesized that a combinat ion including, boswellic acid, betaine and myo-inositol would be able to reduce breast density working on different pathways. PATIENTS AND METHODS: In this study, seventy-six premenopausal women were randomly assigned to the placebo and the experimental drug arms (Eumastós®) for six months. RESULTS: After 6 months of treatment, statistically significant difference between the two groups was recorded on the breast density reduction (60% vs. 9%), using mammographic as well as ultrasound examination. CONCLUSIONS: Preliminary data collected here with support the starting assumptions,that the association comprising boswellic acid, betaine and myo-inositol significantly reduces mammary density, providing the first evidence for a new and safe approach for the management of mammographic density treatment

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

The measurement of household socio-economic position in tuberculosis prevalence surveys: a sensitivity analysis.

OBJECTIVE: To assess the robustness of socio-economic inequalities in tuberculosis (TB) prevalence surveys. DESIGN: Data were drawn from the TB prevalence survey conducted in Lusaka Province, Zambia, in 2005-2006. We compared TB socio-economic inequalities measured through an asset-based index (Index 0) using principal component analysis (PCA) with those observed using three alternative indices: Index 1 and Index 2 accounted respectively for the biases resulting from the inclusion of urban assets and food-related variables in Index 0. Index 3 was built using regression-based analysis instead of PCA to account for the effect of using a different assets weighting strategy. RESULTS: Household socio-economic position (SEP) was significantly associated with prevalent TB, regardless of the index used; however, the magnitude of inequalities did vary across indices. A strong association was found for Index 2, suggesting that the exclusion of food-related variables did not reduce the extent of association between SEP and prevalent TB. The weakest association was found for Index 1, indicating that the exclusion of urban assets did not lead to higher extent of TB inequalities. CONCLUSION: TB socio-economic inequalities seem to be robust to the choice of SEP indicator. The epidemiological meaning of the different extent of TB inequalities is unclear. Further studies are needed to confirm our conclusions

Environmental exposures during windows of susceptibility for breast cancer: a framework for prevention research.

BackgroundThe long time from exposure to potentially harmful chemicals until breast cancer occurrence poses challenges for designing etiologic studies and for implementing successful prevention programs. Growing evidence from animal and human studies indicates that distinct time periods of heightened susceptibility to endocrine disruptors exist throughout the life course. The influence of environmental chemicals on breast cancer risk may be greater during several windows of susceptibility (WOS) in a woman's life, including prenatal development, puberty, pregnancy, and the menopausal transition. These time windows are considered as specific periods of susceptibility for breast cancer because significant structural and functional changes occur in the mammary gland, as well as alterations in the mammary micro-environment and hormone signaling that may influence risk. Breast cancer research focused on these breast cancer WOS will accelerate understanding of disease etiology and prevention.Main textDespite the plausible heightened mechanistic influences of environmental chemicals on breast cancer risk during time periods of change in the mammary gland's structure and function, most human studies of environmental chemicals are not focused on specific WOS. This article reviews studies conducted over the past few decades that have specifically addressed the effect of environmental chemicals and metals on breast cancer risk during at least one of these WOS. In addition to summarizing the broader evidence-base specific to WOS, we include discussion of the NIH-funded Breast Cancer and the Environment Research Program (BCERP) which included population-based and basic science research focused on specific WOS to evaluate associations between breast cancer risk and particular classes of endocrine-disrupting chemicals-including polycyclic aromatic hydrocarbons, perfluorinated compounds, polybrominated diphenyl ethers, and phenols-and metals. We outline ways in which ongoing transdisciplinary BCERP projects incorporate animal research and human epidemiologic studies in close partnership with community organizations and communication scientists to identify research priorities and effectively translate evidence-based findings to the public and policy makers.ConclusionsAn integrative model of breast cancer research is needed to determine the impact and mechanisms of action of endocrine disruptors at different WOS. By focusing on environmental chemical exposure during specific WOS, scientists and their community partners may identify when prevention efforts are likely to be most effective

Rationale, Study Design, and Cohort Characteristics for the Markers for Environmental Exposures (MEE) Study.

Environmental factors have been linked to many diseases and health conditions, but reliable assessment of environmental exposures is challenging. Developing biomarkers of environmental exposures, rather than relying on self-report, will improve our ability to assess the association of such exposures with disease. Epigenetic markers, most notably DNA methylation, have been identified for some environmental exposures, but identification of markers for additional exposures is still needed. The rationale behind the Markers for Environmental Exposures (MEE) Study was to (1) identify biomarkers, especially epigenetic markers, of environmental exposures, such as pesticides, air/food/water contaminants, and industrial chemicals that are commonly encountered in the general population; and (2) support the study of potential relationships between environmental exposures and health and health-related factors. The MEE Study is a cross-sectional study with potential for record linkage and follow-up. The well-characterized cohort of 400 postmenopausal women has generated a repository of biospecimens, including blood, urine, and saliva samples. Paired data include an environmental exposures questionnaire, a breast health questionnaire, dietary recalls, and a food frequency questionnaire. This work describes the rationale, study design, and cohort characteristics of the MEE Study. In addition to our primary research goals, we hope that the data and biorepository generated by this study will serve as a resource for future studies and collaboration

Breast cancer risk is increased in the years following false-positive breast cancer screening

A small number of studies have investigated breast cancer (BC) risk among women with a history of false-positive recall (FPR) in BC screening, but none of them has used time-to-event analysis while at the same time quantifying the effect of false-negative diagnostic assessment (FNDA). FNDA occurs when screening detects BC, but this BC is missed on diagnostic assessment (DA). As a result of FNDA, screenings that detected cancer are incorrectly classified as FPR. Our study linked data recorded in the Flemish BC screening program (women aged 50-69 years) to data from the national cancer registry. We used Cox proportional hazards models on a retrospective cohort of 298 738 women to assess the association between FPR and subsequent BC, while adjusting for potential confounders. The mean follow-up was 6.9 years. Compared with women without recall, women with a history of FPR were at an increased risk of developing BC [hazard ratio = 2.10 (95% confidence interval: 1.92-2.31)]. However, 22% of BC after FPR was due to FNDA. The hazard ratio dropped to 1.69 (95% confidence interval: 1.52-1.87) when FNDA was excluded. Women with FPR have a subsequently increased BC risk compared with women without recall. The risk is higher for women who have a FPR BI-RADS 4 or 5 compared with FPR BI- RADS 3. There is room for improvement of diagnostic assessment: 41% of the excess risk is explained by FNDA after baseline screening