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Breast cancer facts, myths, and controversies : understanding current screenings and treatments
\"Using clear, reader-friendly text, this book explains this vital and ever-evolving field, with the aim of helping women make informed decisions by understanding breast cancer, associated screenings and treatments, as well as their benefits, risks, and limitations\"-- Provided by publisher.
Book
The fraction of breast cancer attributable to smoking: The Norwegian women and cancer study 1991–2012
Background:
Results from several recent cohort studies on smoking and breast cancer incidence and mortality suggest that the burden of smoking on society is underestimated. We estimated the fraction of breast cancer attributable to smoking in the Norwegian Women and Cancer Study, a nationally representative prospective cohort study.
Methods:
We followed 130 053 women, aged 34–70 years, who completed a baseline questionnaire between 1991 and 2007, through linkages to national registries through December 2012. We used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs), while adjusting for confounders. Never smokers, excluding passive smokers, were used as the reference group in all main analyses. We estimated attributable fractions (AFs) % in smokers and in the population (PAFs) % with 95% CIs.
Results:
Altogether, 4293 women developed invasive breast cancer, confirmed by histology. Compared with never active, never passive smokers, ever (former and current) smokers had an overall risk of breast cancer that was 21% higher (HR=1.21; 95% CI=1.08–1.34). For ever smokers, the AF was 17.3% (95% CI =7.4–25.4) and for the population the PAF of breast cancer was 11.9% (95% CI=5.3–18.1). For passive smokers, the PAF of breast cancer was 3.2% (95% CI=1.0–5.4). When we applied PAF estimates for ever smoking on the 2907 new breast cancer cases among Norwegian women aged 35+ at diagnosis in 2012, this yielded 345 (95% CI=154–526) breast cancer cases that could have been avoided in the absence of active smoking that year.
Conclusions:
In smokers, one in six and in the population, one in nine breast cancer cases could have been avoided in the absence of active smoking. Our findings support the notion that the global cancer burden due to smoking is substantially underestimated.
Journal Article
Ensemble‐based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long‐term risk of metastases
Background Current clinical markers overestimate the recurrence risk in many lymph node negative (LNN) breast cancer (BC) patients such that a majority of these low‐risk patients unnecessarily receive systemic treatments. We tested if differential microRNA expression in primary tumors allows reliable identification of indolent LNN BC patients to provide an improved classification tool for overtreatment reduction in this patient group. Methods We collected freshly frozen primary tumors of 80 LNN BC patients with recurrence and 80 recurrence‐free patients (mean follow‐up: 20.9 years). The study comprises solely systemically untreated patients to exclude that administered treatments confound the metastasis status. Samples were pairwise matched for clinical‐pathological characteristics to minimize dependence of current markers. Patients were classified into risk‐subgroups according to the differential microRNA expression of their tumors via classification model building with cross‐validation using seven classification methods and a voting scheme. The methodology was validated using available data of two independent cohorts (n = 123, n = 339). Results Of the 80 indolent patients (who would all likely receive systemic treatments today) our ultralow‐risk classifier correctly identified 37 while keeping a sensitivity of 100% in the recurrence group. Multivariable logistic regression analysis confirmed independence of voting results from current clinical markers. Application of the method in two validation cohorts confirmed successful classification of ultralow‐risk BC patients with significantly prolonged recurrence‐free survival. Conclusion Profiles of differential microRNAs expression can identify LNN BC patients who could spare systemic treatments demanded by currently applied classifications. However, further validation studies are required for clinical implementation of the applied methodology. Our study shows that differential microRNA expression analysis has the potential to identify a subgroup of Breast cancer (BC) patients with an extremely low long‐term risk of metastases. The ensemble‐based classification approach developed could form the starting point for a diagnostic tool to avoid unnecessary overtreatment of a significant proportion of BC patients.
Journal Article
Exploring the Role of Mutations in Fanconi Anemia Genes in Hereditary Cancer Patients
Fanconi anemia (FA) is caused by biallelic mutations in FA genes. Monoallelic mutations in five of these genes (BRCA1, BRCA2, PALB2, BRIP1 and RAD51C) increase the susceptibility to breast/ovarian cancer and are used in clinical diagnostics as bona-fide hereditary cancer genes. Increasing evidence suggests that monoallelic mutations in other FA genes could predispose to tumor development, especially breast cancer. The objective of this study is to assess the mutational spectrum of 14 additional FA genes (FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FANCP, FANCQ, FANCR and FANCU) in a cohort of hereditary cancer patients, to compare with local cancer-free controls as well as GnomAD. A total of 1021 hereditary cancer patients and 194 controls were analyzed using our next generation custom sequencing panel. We identified 35 pathogenic variants in eight genes. A significant association with the risk of breast cancer/breast and ovarian cancer was found for carriers of FANCA mutations (odds ratio (OR) = 3.14 95% confidence interval (CI) 1.4–6.17, p = 0.003). Two patients with early-onset cancer showed a pathogenic FA variant in addition to another germline mutation, suggesting a modifier role for FA variants. Our results encourage a comprehensive analysis of FA genes in larger studies to better assess their role in cancer risk.
Journal Article
Association between adjuvant radiation treatment and breast cancer‐specific mortality among older women with comorbidity burden: A comparative effectiveness analysis of SEER‐MHOS
Background The National Comprehensive Cancer Network suggested that older women with low‐risk breast cancer (LRBC; i.e., early‐stage, node‐negative, and estrogen receptor‐positive) could omit adjuvant radiation treatment (RT) after breast‐conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer‐specific mortality among older women with comorbidity is not fully known. Methods 1105 older women (≥65 years) with LRBC in 1998–2012 were queried from the Surveillance, Epidemiology, and End Results–Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score‐based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer‐specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class. Results Three comorbidity burden (low, moderate, and high) groups were identified. A total of 318 deaths (47 cancer‐related) occurred. The IPTW‐adjusted Cox regression analysis showed that RT omission was not associated with short‐term, 5‐ and 10‐year cancer‐specific death (p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long‐term cancer‐specific death in women with low comorbidity burden (HR = 1.98, 95% CI = 1.17, 3.33), which warrants further study. Conclusions Omission of RT after BCS is not associated with an increased risk of cancer‐specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden.
Journal Article
Satisfaction and Quality of Life of Healthy and Unilateral Diseased BRCA1/2 Pathogenic Variant Carriers after Risk-Reducing Mastectomy and Reconstruction Using the BREAST-Q Questionnaire
Risk-reducing mastectomy (RRM) is the most efficient form of breast cancer (BC) risk reduction in BRCA1/2 pathogenic variant (pV) carriers. However, this intervention in physical integrity is associated with significant morbidity. We assessed long-term perception of satisfaction and health-related quality of life (QoL) after bilateral RRM and reconstruction using the validated BREAST-Q. We searched the prospective database of the Center for Hereditary Breast and Ovarian Cancer Cologne for previvors and survivors who underwent bilateral RRM from 1994 to 2015 and evaluated the results of their BREAST-Q scores. The study enrolled 43 previvors and 90 survivors after a mean follow-up of 46.3 ± 45.3 months after RRM. Satisfaction and QoL were independent of the technique of RRM or type of reconstruction but depended on the time of RRM. Compared to survivors, previvors had significantly higher mean satisfaction scores in their psychosocial, sexual, and physical well-being (chest) in both modules. Among previvors and survivors, higher psychological well-being correlated with a higher satisfaction with information and higher satisfaction with outcome. As psychological well-being correlated with satisfaction with information and outcome, we developed decision aids to improve shared decision making and long-term satisfaction with the decision and the postoperative outcome.
Journal Article
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Background
Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.
Main body
This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman’s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.
Conclusions
We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
Journal Article
Mammographic density—a review on the current understanding of its association with breast cancer
There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords ‘mammographic dens*’, ‘dense mammary tissue’ or ‘percent dens*’ were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD’s association with BC risk.
Journal Article
Association of breast cancer risk, density, and stiffness: global tissue stiffness on breast MR elastography (MRE)
Purpose
Quantify in vivo biomechanical tissue properties in various breast densities and in average risk and high-risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk based on patient demographics and clinical data.
Methods
Patients with average risk or high-risk of breast cancer underwent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (average versus high). Separately for stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors.
Results
50 average risk and 86 high-risk patients were included. Risk groups were similar in age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high-risk patients (
N
= 55) compared to average risk patients (
N
= 34; all
p
< 0.001). Stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.
Conclusion
A structurally based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high-risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.
Journal Article