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1.
Int J Cancer ; 150(1): 73-79, 2022 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-34460111

RESUMO

Polygenic risk scores (PRS) for disease risk stratification show great promise for application in general populations, but most are based on data from individuals of White European origin. We assessed two well validated PRS (SNP18, SNP143) in the Predicting-Risk-of-Cancer-At-Screening (PROCAS) study in North-West England for breast cancer prediction based on ethnicity. Overall, 9475 women without breast cancer at study entry, including 645 who subsequently developed invasive breast cancer or ductal carcinoma in situ provided DNA. All were genotyped for SNP18 and a subset of 1868 controls were genotyped for SNP143. For White Europeans both PRS discriminated well between individuals with and without cancer. For n = 395 Black (n = 112), Asian (n = 119), mixed (n = 44) or Jewish (n = 120) women without cancer both PRS overestimated breast cancer risk, being most marked for women of Black and Jewish origin (P < .001). SNP143 resulted in a potential mean 40% breast cancer risk overestimation in the combined group of non-White/non-European origin. SNP-PRS that has been normalized based on White European ethnicity for breast cancer should not be used to predict risk in women of other ethnicities. There is an urgent need to develop PRS specific for other ethnicities, in order to widen access of this technology.


Assuntos
Biomarcadores Tumorais/genética , Neoplasias da Mama/epidemiologia , Carcinoma Ductal de Mama/epidemiologia , Carcinoma Intraductal não Infiltrante/epidemiologia , Polimorfismo de Nucleotídeo Único , /genética , Adulto , Idoso , Densidade da Mama , Neoplasias da Mama/genética , Neoplasias da Mama/patologia , Carcinoma Ductal de Mama/genética , Carcinoma Ductal de Mama/patologia , Carcinoma Intraductal não Infiltrante/genética , Carcinoma Intraductal não Infiltrante/patologia , Estudos de Casos e Controles , Inglaterra/epidemiologia , Feminino , Seguimentos , Predisposição Genética para Doença , Humanos , Pessoa de Meia-Idade , Prognóstico , Fatores de Risco
3.
PLoS One ; 16(10): e0258212, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34618839

RESUMO

The ectodysplasin receptor (EDAR) is a tumor necrosis factor receptor (TNF) superfamily member. A substitution in an exon of EDAR at position 370 (EDARV370A) creates a gain of function mutant present at high frequencies in Asian and Indigenous American populations but absent in others. Its frequency is intermediate in populations of Mexican ancestry. EDAR regulates the development of ectodermal tissues, including mammary ducts. Obesity and type 2 diabetes mellitus are prevalent in people with Indigenous and Latino ancestry. Latino patients also have altered prevalence and presentation of breast cancer. It is unknown whether EDARV370A might connect these phenomena. The goals of this study were to determine 1) whether EDARV370A is associated with metabolic phenotypes and 2) if there is altered breast anatomy in women carrying EDARV370A. Participants were from two Latino cohorts, the Arizona Insulin Resistance (AIR) registry and Sangre por Salud (SPS) biobank. The frequency of EDARV370A was 47% in the Latino cohorts. In the AIR registry, carriers of EDARV370A (GG homozygous) had significantly (p < 0.05) higher plasma triglycerides, VLDL, ALT, 2-hour post-challenge glucose, and a higher prevalence of prediabetes/diabetes. In a subset of the AIR registry, serum levels of ectodysplasin A2 (EDA-A2) also were associated with HbA1c and prediabetes (p < 0.05). For the SPS biobank, participants that were carriers of EDARV370A had lower breast density and higher HbA1c (both p < 0.05). The significant associations with measures of glycemia remained when the cohorts were combined. We conclude that EDARV370A is associated with characteristics of the metabolic syndrome and breast density in Latinos.


Assuntos
Densidade da Mama/genética , Receptor Edar/genética , Estudos de Associação Genética , Predisposição Genética para Doença , Síndrome Metabólica/genética , Mutação/genética , Adulto , Comitês Consultivos , Arizona , Bancos de Espécimes Biológicos , Glicemia/metabolismo , Ectodisplasinas/genética , Feminino , Frequência do Gene/genética , Hemoglobina A Glicada/metabolismo , Humanos , Resistência à Insulina , Masculino , Síndrome Metabólica/sangue , Pessoa de Meia-Idade , Sistema de Registros
4.
Comput Methods Programs Biomed ; 212: 106443, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34656014

RESUMO

BACKGROUND AND OBJECTIVES: The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today. METHODS: We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face. RESULTS: Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered. CONCLUSIONS: Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.


Assuntos
Mamografia , Neoplasias , Algoritmos , Densidade da Mama , Curva ROC , Medição de Risco
5.
Nihon Hoshasen Gijutsu Gakkai Zasshi ; 77(10): 1165-1172, 2021.
Artigo em Japonês | MEDLINE | ID: mdl-34670923

RESUMO

BACKGROUND: In the field of breast screening using mammography, announcing to the examinees whether they are dense or not has not been deprecated in Japan. One of the reasons is a shortage of objectivity estimating their dense breast. Our aim is to build a system with deep learning algorithm to calculate and quantify objective breast density automatically. MATERIAL AND METHOD: Mammography images taken in our institute that were diagnosed as category 1 were collected. Each processed image was transformed into eight-bit grayscale, with the size of 2294 pixels by 1914 pixels. The "base pixel value" was calculated from the fatty area within the breast for each image. The "relative density" was calculated by dividing each pixel value by the base pixel value. Semantic segmentation algorithm was used to automatically segment the area of breast tissue within the mammography image, which was resized to 144 pixels by 120 pixels. By aggregating the relative density within the breast tissue area, the "breast density" was obtained automatically. RESULT: From each but one mammography image, the breast density was successfully calculated automatically. By defining a dense breast as the breast density being greater than or equal to 30%, the evaluation of the dense breast was consistent with that by a computer and human (76.6%). CONCLUSION: Deep learning provides an excellent estimation of quantification of breast density. This system could contribute to improve the efficiency of mammography screening system.


Assuntos
Densidade da Mama , Aprendizado Profundo , Algoritmos , Detecção Precoce de Câncer , Humanos , Mamografia
6.
Nihon Hoshasen Gijutsu Gakkai Zasshi ; 77(10): 1209-1216, 2021.
Artigo em Japonês | MEDLINE | ID: mdl-34670929

RESUMO

We analyzed the compression pressures in 2772 mammography images of 807 patients acquired by digital mammography equipment at four facilities. The analysis included the average compression pressure at all facilities, difference in compression pressure at each facility, differences between the pressures used by radiological technologists in the same facility, and difference attributed to the breast structure. We also analyzed the effects of the compression pressure on the breast thickness and mean glandular dose (MGD) at each facility. The median values of the compression pressure and breast thickness for the 2772 images at all facilities were 86.5 N and 43 mm, respectively. The compression pressures differed among the institutions. The maximum difference in the median pressures among the four facilities was 38.6 N, while the difference in the breast thickness was 6 mm. The radiological technologists working at the same facility used almost the same compression pressure. However, differences between the compression pressures used by different radiological technologists were observed. The compression pressure in a dense breast was smaller than that in a non-dense breast. The difference in the compression pressure affected the breast thickness and MGD. The results of this analysis could be utilized for an optimal imaging in future digital mammography.


Assuntos
Neoplasias da Mama , Mamografia , Mama/diagnóstico por imagem , Densidade da Mama , Feminino , Humanos , Pressão
7.
Cochrane Database Syst Rev ; 10: CD013091, 2021 10 26.
Artigo em Inglês | MEDLINE | ID: mdl-34697802

RESUMO

BACKGROUND: Endocrine therapy is effective at preventing or treating breast cancer. Some forms of endocrine therapy have been shown to reduce mammographic density. Reduced mammographic density for women receiving endocrine therapy could be used to estimate the chance of breast cancer returning or developing breast cancer in the first instance (a prognostic biomarker). In addition, changes in mammographic density might be able to predict how well a woman responds to endocrine therapy (a predictive biomarker). The role of breast density as a prognostic or predictive biomarker could help improve the management of breast cancer. OBJECTIVES: To assess the evidence that a reduction in mammographic density following endocrine therapy for breast cancer prevention in women without previous breast cancer, or for treatment in women with early-stage hormone receptor-positive breast cancer, is a prognostic or predictive biomarker. SEARCH METHODS: We searched the Cochrane Breast Cancer Group Specialised Register, CENTRAL, MEDLINE, Embase, and two trials registers on 3 August 2020 along with reference checking, bibliographic searching, and contact with study authors to obtain further data. SELECTION CRITERIA: We included randomised, cohort and case-control studies of adult women with or without breast cancer receiving endocrine therapy. Endocrine therapy agents included were selective oestrogen receptor modulators and aromatase inhibitors. We required breast density before start of endocrine therapy and at follow-up. We included studies published in English. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures expected by Cochrane. Two review authors independently extracted data and assessed risk of bias using adapted Quality in Prognostic Studies (QUIPS) and Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tools. We used the GRADE approach to evaluate the certainty of the evidence. We did not perform a quantitative meta-analysis due to substantial heterogeneity across studies. MAIN RESULTS: Eight studies met our inclusion criteria, of which seven provided data on outcomes listed in the protocol (5786 women). There was substantial heterogeneity across studies in design, sample size (349 to 1066 women), participant characteristics, follow-up (5 to 14 years), and endocrine therapy agent. There were five breast density measures and six density change definitions. All studies had at least one domain as at moderate or high risk of bias. Common concerns were whether the study sample reflected the review target population, and likely post hoc definitions of breast density change. Most studies on prognosis for women receiving endocrine therapy reported a reduced risk associated with breast density reduction. Across endpoints, settings, and agents, risk ratio point estimates (most likely value) were between 0.1 and 1.5, but with substantial uncertainty. There was greatest consistency in the direction and magnitude of the effect for tamoxifen (across endpoints and settings, risk ratio point estimates were between 0.3 and 0.7). The findings are summarised as follows. Prognostic biomarker findings: Treatment Breast cancer mortality Two studies of 823 women on tamoxifen (172 breast cancer deaths) reported risk ratio point estimates of ~0.4 and ~0.5 associated with a density reduction. The certainty of the evidence was low. Recurrence Two studies of 1956 women on tamoxifen reported risk ratio point estimates of ~0.4 and ~0.7 associated with a density reduction. There was risk of bias in methodology for design and analysis of the studies and considerable uncertainty over the size of the effect. One study of 175 women receiving an aromatase inhibitor reported a risk ratio point estimate of ~0.1 associated with a density reduction. There was considerable uncertainty about the effect size and a moderate or high risk of bias in all domains. One study of 284 women receiving exemestane or tamoxifen as part of a randomised controlled trial reported risk ratio point estimates of ~1.5 (loco-regional recurrence) and ~1.3 (distance recurrence) associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the size of the effects. The certainty of the evidence for all recurrence endpoints was very low. Incidence of a secondary primary breast cancer Two studies of 451 women on exemestane, tamoxifen, or unknown endocrine therapy reported risk ratio point estimates of ~0.5 and ~0.6 associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the effect size. The certainty of the evidence was very low. We were unable to find data regarding the remaining nine outcomes prespecified in the review protocol. Prevention Incidence of invasive breast cancer and ductal carcinoma in situ (DCIS) One study of 507 women without breast cancer who were receiving preventive tamoxifen as part of a randomised controlled trial (51 subsequent breast cancers) reported a risk ratio point estimate of ~0.3 associated with a density reduction. The certainty of the evidence was low. Predictive biomarker findings: One study of a subset of 1065 women from a randomised controlled trial assessed how much the effect of endocrine therapy could be explained by breast density declines in those receiving endocrine therapy. This study evaluated the prevention of invasive breast cancer and DCIS. We found some evidence to support the hypothesis, with a risk ratio interaction point estimate ~0.5. However, the 95% confidence interval included unity, and data were based on 51 women with subsequent breast cancer in the tamoxifen group. The certainty of the evidence was low. AUTHORS' CONCLUSIONS: There is low-/very low-certainty evidence to support the hypothesis that breast density change following endocrine therapy is a prognostic biomarker for treatment or prevention. Studies suggested a potentially large effect size with tamoxifen, but the evidence was limited. There was less evidence that breast density change following tamoxifen preventive therapy is a predictive biomarker than prognostic biomarker. Evidence for breast density change as a prognostic treatment biomarker was stronger for tamoxifen than aromatase inhibitors. There were no studies reporting mammographic density change following endocrine therapy as a predictive biomarker in the treatment setting, nor aromatase inhibitor therapy as a prognostic or predictive biomarker in the preventive setting. Further research is warranted to assess mammographic density as a biomarker for all classes of endocrine therapy and review endpoints.


Assuntos
Densidade da Mama , Neoplasias da Mama , Biomarcadores , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Feminino , Humanos , Prognóstico , Ensaios Clínicos Controlados Aleatórios como Assunto , Tamoxifeno
8.
Breast Cancer Res Treat ; 190(3): 451-462, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34570302

RESUMO

PURPOSE: Change in mammographic density has been suggested to be a proxy of tamoxifen response. We investigated the effect of additional adjuvant systemic therapy and CYP2D6 activity on MD change in a cohort of tamoxifen-treated pre- and postmenopausal breast cancer patients. METHODS: Swedish breast cancer patients (n = 699)  operated 2006-2014, genotyped for CYP2D6, having at least three months postoperative tamoxifen treatment, a baseline, and at least one follow-up digital mammogram were included in the study. Other systemic adjuvant treatment included chemotherapy, goserelin, and aromatase inhibitors. Change in MD, dense area, was assessed using the automated STRATUS method. Patients were stratified on baseline characteristics, treatments, and CYP2D6 activity (poor, intermediate, extensive, and ultrarapid). Relative density change was calculated at year 1, 2, and 5 during follow-up in relation to treatments and CYP2D6 activity. RESULTS: Mean relative DA decreased under the follow-up period, with a more pronounced MD reduction in premenopausal patients. No significant effect of chemotherapy, aromatase inhibitors, goserelin, or CYP2D6 activity on DA change was found. DA did not revert to baseline levels after tamoxifen discontinuation. CONCLUSION: Our results indicate that other systemic adjuvant therapy does not further reduce MD in tamoxifen-treated breast cancer patients. We could not confirm the previously suggested association between CYP2D6 activity and MD reduction in a clinical setting with multimodality adjuvant treatment. No rebound effect on MD decline after tamoxifen discontinuation was evident.


Assuntos
Neoplasias da Mama , Tamoxifeno , Antineoplásicos Hormonais/uso terapêutico , Densidade da Mama , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Quimioterapia Adjuvante , Citocromo P-450 CYP2D6/genética , Feminino , Genótipo , Humanos , Tamoxifeno/uso terapêutico
9.
Radiology ; 301(3): 569-570, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34519580
10.
Comput Methods Programs Biomed ; 211: 106368, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34537490

RESUMO

BACKGROUND AND OBJECTIVE: Breast density refers to the proportion of glandular and fatty tissue in the breast and is recognized as a useful factor assessing breast cancer risk. Moreover, the segmentation of the high-density glandular tissue from mammograms can assist medical professionals visualizing and localizing areas that may require additional attention. Developing robust methods to segment breast tissues is challenging due to the variations in mammographic acquisition systems and protocols. Deep learning methods are effective in medical image segmentation but they often require large quantities of labelled data. Unsupervised domain adaptation is an area of research that employs unlabelled data to improve model performance on variations of samples derived from different sources. METHODS: First, a U-Net architecture was used to perform segmentation of the fatty and glandular tissues with labelled data from a single acquisition device. Then, adversarial-based unsupervised domain adaptation methods were used to incorporate single unlabelled target domains, consisting of images from a different machine, into the training. Finally, the domain adaptation model was extended to include multiple unlabelled target domains by combining a reconstruction task with adversarial training. RESULTS: The adversarial training was found to improve the generalization of the initial model on new domain data, demonstrating clearly improved segmentation of the breast tissues. For training with multiple unlabelled domains, combining a reconstruction task with adversarial training improved the stability of the training and yielded adequate segmentation results across all domains with a single model. CONCLUSIONS: Results demonstrated the potential for adversarial-based domain adaptation with U-Net architectures for segmentation of breast tissue in mammograms coming from several devices and demonstrated that domain-adapted models could achieve a similar agreement with manual segmentations. It has also been found that combining adversarial and reconstruction-based methods can provide a simple and effective solution for training with multiple unlabelled target domains.


Assuntos
Processamento de Imagem Assistida por Computador , Mamografia , Tecido Adiposo , Mama/diagnóstico por imagem , Densidade da Mama
12.
Breast Cancer Res Treat ; 190(2): 343-353, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34529194

RESUMO

PURPOSE: While increased breast density is a risk factor for breast cancer, the effect of fatty liver disease on breast density is unknown. We investigated whether fatty liver is a risk factor for changes in breast density over ~ 4 years of follow-up in pre- and postmenopausal women. METHODS: This study included 74,781 middle-aged Korean women with mammographically determined dense breasts at baseline. Changes in dense breasts were identified by more screening mammograms during follow-up. Hepatic steatosis (HS) was measured using ultrasonography. Flexible parametric proportional hazards models were used to determine the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs), and a Weibull accelerated failure time model (AFT) was used to determine the time ratios (TRs) and 95% CIs. RESULTS: During a median follow-up of 4.1 years, 4022 women experienced resolution of the dense breasts. The association between HS and dense breast resolution differed by the menopause status (P for interaction < 0.001). After adjusting for body mass index and other covariates, the aHRs (95% CI) for dense breast resolution comparing HS to non-HS were 0.81 (0.70-0.93) in postmenopausal women, while the association was converse in premenopausal women with the corresponding HRs of 1.30 (1.18-1.43). As an alternative approach, the multivariable-adjusted TR (95% CI) for dense breast survival comparing HS to non-HS were 0.81 (0.75-0.87) and 1.19 (1.06-1.33) in premenopausal and postmenopausal women, respectively. CONCLUSION: The association between HS and changes in dense breasts differed with the menopause status. HS increased persistent dense breast survival in postmenopausal women but decreased it in premenopausal women.


Assuntos
Neoplasias da Mama , Hepatopatias , Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Feminino , Humanos , Mamografia , Pessoa de Meia-Idade , Pós-Menopausa , Fatores de Risco
13.
Clin Imaging ; 80: 315-321, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34482242

RESUMO

OBJECTIVE: Compare the BI-RADS 3 rate and follow-up of dense breast ultrasound (US) screening following digital mammography (DM) versus digital breast tomosynthesis (DBT). METHODS: IRB-approved, HIPAA compliant retrospective search was performed of databases at two tertiary breast centers and an office practice for BI-RADS 3 screening US examinations performed 10/1/14-9/30/16. Prior DM versus DBT, downgrade and upgrade rate, and timing and pathology results were recorded. Differences were compared using the two-sample proportions test. RESULTS: 3183 screening US examinations were performed, 1434/3183 (45.1%) after DM and 1668/3183 (52%) after DBT (2.5% (81/3183) no prior mammogram available). 13.9% (199/1434) had BI-RADS 3 results after DM and 10.6% (177/1668) after DBT (p < 0.01). Median imaging follow-up after DM was 12 months (IQR 6, 24) versus 18 after DBT (IQR 11, 25), p = 0.02. 19.5% (73/375) of patients were lost to follow-up (19.2% (38/198) after DM (68.4% (26/38) no follow-up after initial exam) versus 19.8% (35/177) after DBT (54.3% (19/35) no follow-up after initial exam). 1.3% (5/375) of patients elected biopsy (1.5% (3/198) after DM and 1.1% (2/177) after DBT). 75.2% (282/375) of patients were downgraded (75.3% (149/198) after DM and 75.1% (133/177) after DBT). 2.5% (5/198) were upgraded after DM and 0.6% (1/177) after DBT. Median time to upgrade was 6 months after both DM and DBT. 0.3% (1/375) of patients with BI-RADS 3 results had cancer on follow-up. CONCLUSION: Patients with prior DBT had a lower risk of encountering BI-RADS 3 findings on screening ultrasound. BI-RADS 3 findings on screening ultrasound had an extremely low rate of being cancer.


Assuntos
Neoplasias da Mama , Mamografia , Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Estudos Retrospectivos , Ultrassonografia Mamária
14.
Ned Tijdschr Geneeskd ; 1652021 07 29.
Artigo em Holandês | MEDLINE | ID: mdl-34346656

RESUMO

One-size-fits-all breast cancer screening no longer reflects the current state of knowledge and technology. 8% of the participants in the Dutch Breastcancer Screening Program have extremely dense breasts, which is coupled to a strongly increased risk of breast cancer. In addition, for this group of approximately 80,000 women per year, mammography is only 60% sensitive. The DENSE trial showed that supplemental MRI after a negative mammogram reduced the number of interval cancers by more than 80%. The Dutch Health Council however subsequently recommended to consider contrast-enhanced mammography (CEM) as a screening tool. At the request of the Ministry of Health-Welfare and Sport, simultaneous research is being set up to study both CEM and the introduction of CEM and "accelerated" (abbreviated) MRI. This article explains the differences between the two techniques and discusses the role both could play in screening this large group of women when politicians give green light.


Assuntos
Neoplasias da Mama , Mamografia , Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Detecção Precoce de Câncer , Feminino , Humanos , Imageamento por Ressonância Magnética , Programas de Rastreamento , Países Baixos
15.
Am J Prev Med ; 61(6): 890-899, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34376293

RESUMO

INTRODUCTION: Many states have mandated breast density notification and insurance coverage for additional screening; yet, the association between such legislation and stage of diagnosis for breast cancer is unclear. This study investigates this association and examines the differential impacts among different age and race/ethnicity subgroups. METHODS: The Surveillance, Epidemiology, and End Results database was queried to identify patients with breast cancer aged 40-74 years diagnosed between 2005 and 2016. Using a difference-in-differences multinomial logistic model, the odds of being diagnosed at different stages of cancer relative to the localized stage depending on legislation and individual characteristics were examined. Analyses were conducted in 2020-2021. RESULTS: The study included 689,641 cases. Overall, the impact of notification legislation was not significant, whereas insurance coverage legislation was associated with 6% lower odds (OR=0.94, 95% CI=0.91, 0.96) of being diagnosed at the regional stage. The association between insurance coverage legislation and stage of diagnosis was even stronger among women aged 40-49 years, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.96) of being diagnosed at the regional stage and 12% lower odds (OR=0.88, 95% CI=0.81, 0.96) of being diagnosed at the distant stage. Hispanic women benefited from notification laws, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.97) of being diagnosed at distant stage. Neither notification nor supplemental screening insurance coverage legislation showed a substantial impact on Black women. CONCLUSIONS: The findings imply that improving insurance coverage is more important than being notified overall. Raising awareness is important among Hispanic women; improving communication about dense breasts and access to screening might be more important than legislation among Black women.


Assuntos
Densidade da Mama , Neoplasias da Mama , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/epidemiologia , Detecção Precoce de Câncer , Feminino , Humanos , Mamografia , Programas de Rastreamento
16.
Breast Cancer Res Treat ; 190(1): 69-78, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34383179

RESUMO

PURPOSE: Obesity is a known risk factor for post-menopausal breast cancer and may increase risk for triple negative breast cancer in premenopausal women. Intervention strategies are clearly needed to reduce obesity-associated breast cancer risk. METHODS: We conducted a Phase II double-blind, randomized, placebo-controlled trial of metformin in overweight/obese premenopausal women with components of metabolic syndrome to assess the potential of metformin for primary breast cancer prevention. Eligible participants were randomized to receive metformin (850 mg BID, n = 76) or placebo (n = 75) for 12 months. Outcomes included breast density, assessed by fat/water MRI with change in percent breast density as the primary endpoint, anthropometric measures, and intervention feasibility. RESULTS: Seventy-six percent in the metformin arm and 83% in the placebo arm (p = 0.182) completed the 12-month intervention. Adherence to study agent was high with more than 80% of participants taking ≥ 80% assigned pills. The most common adverse events reported in the metformin arm were gastrointestinal in nature and subsided over time. Compared to placebo, metformin intervention led to a significant reduction in waist circumference (p < 0.001) and waist-to-hip ratio (p = 0.019). Compared to placebo, metformin did not change percent breast density and dense breast volume but led to a numerical but not significant decrease in non-dense breast volume (p = 0.070). CONCLUSION: We conclude that metformin intervention resulted in favorable changes in anthropometric measures of adiposity and a borderline decrease in non-dense breast volume in women with metabolic dysregulation. More research is needed to understand the impact of metformin on breast cancer risk reduction. TRIAL REGISTRATION: ClinicalTrials.gov NCT02028221. Registered January 7, 2014, https://clinicaltrials.gov/ct2/show/NCT02028221.


Assuntos
Neoplasias da Mama , Síndrome Metabólica , Metformina , Adiposidade , Densidade da Mama , Neoplasias da Mama/complicações , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/epidemiologia , Estudos de Viabilidade , Feminino , Humanos , Mamografia , Síndrome Metabólica/complicações , Síndrome Metabólica/tratamento farmacológico , Síndrome Metabólica/epidemiologia , Metformina/efeitos adversos , Obesidade/complicações , Obesidade/tratamento farmacológico
17.
Radiology ; 301(2): 283-292, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34402665

RESUMO

Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.


Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Idoso , Mama/diagnóstico por imagem , Reações Falso-Positivas , Feminino , Humanos , Pessoa de Meia-Idade , Países Baixos , Estudos Prospectivos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
19.
BMJ Open ; 11(8): e047513, 2021 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-34408038

RESUMO

OBJECTIVES: To understand general practitioners' (GPs') awareness and knowledge of mammographic breast density (BD) and their perspectives around information and potential notification of BD for women. DESIGN: Qualitative study using semistructured telephone interviews. Interviews were audiorecorded, transcribed and analysed using framework analysis. SETTING: Australia. PARTICIPANTS: Australian GPs (n=30). RESULTS: GPs had limited knowledge of BD and little experience discussing BD with women. There were mixed views on notification of BD with some GPs believing this information would help informed decision making about breast health and that women have the right to know any information about their bodies. While others were concerned about causing unnecessary anxiety and were worried about the uncertainty about what to advise women to do with this information, particularly in relation to supplemental breast screening. The need for an equitable system where all women are either notified or not, and also provided with publicly funded supplemental screening was raised by GPs. Overall, there was high interest in education, training and support around the topic of BD. CONCLUSIONS: Australian GPs require education, support and evidence-based guidelines to have discussions with women with dense breasts and help manage their risk, especially if widespread notification is to be introduced in population-based screening programmes.


Assuntos
Densidade da Mama , Clínicos Gerais , Austrália , Feminino , Humanos , Programas de Rastreamento , Pesquisa Qualitativa
20.
Sci Rep ; 11(1): 16785, 2021 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-34408263

RESUMO

Mammographic density (MD) of the breast and body mass index (BMI) are inversely associated with each other, but have inconsistent associations with respect to the risk of breast cancer. Skeletal muscle mass index (SMI) has been considered to reflect a relatively accurate fat and muscle percentage in the body. So, we evaluated the relation between SMI and MD. A cross-sectional study was performed in 143,456 women who underwent comprehensive examinations from 2012 to 2016. BMI was adjusted to analyze whether SMI is an independent factor predicting dense breast. After adjustment for confounding factors including BMI, the odds ratios for MD for the dense breasts was between the highest and lowest quartiles of SMI at 2.65 for premenopausal women and at 2.39 for postmenopausal women. SMI was a significant predictor for MD, which could be due to the similar growth mechanism of the skeletal muscle and breast parenchymal tissue. Further studies are needed to understand the causal link between muscularity, MD and breast cancer risk.


Assuntos
Neoplasias da Mama/diagnóstico , Mama/diagnóstico por imagem , Glândulas Mamárias Humanas/diagnóstico por imagem , Músculo Esquelético/diagnóstico por imagem , Adulto , Índice de Massa Corporal , Mama/patologia , Densidade da Mama/fisiologia , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Humanos , Glândulas Mamárias Humanas/patologia , Mamografia , Pessoa de Meia-Idade , Músculo Esquelético/patologia , Pós-Menopausa/fisiologia , Pré-Menopausa/fisiologia
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