Risk factors and hormone-receptor status: epidemiology, risk-prediction models and treatment implications for breast cancer

2007 ◽  
Vol 4 (7) ◽  
pp. 415-423 ◽  
Author(s):  
Wendy Y Chen ◽  
Graham A Colditz
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Prediction ◽  
Hormone Receptor ◽  
Prediction Models ◽  
Hormone Receptor Status ◽  
Risk Prediction Models ◽  
Receptor Status

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

scholarly journals Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

2021 ◽  
Author(s):  
Geunwon Kim ◽  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cancer Risk Assessment ◽  
Risk Prediction Models ◽  
Breast Cancer Risk Assessment

Abstract Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman’s breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

scholarly journals Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

2012 ◽  
Vol 49 (9) ◽  
pp. 601-608 ◽  
Author(s):  
Anika Hüsing ◽  
Federico Canzian ◽  
Lars Beckmann ◽  
Montserrat Garcia-Closas ◽  
W Ryan Diver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Hormone Receptor ◽  
Genetic Risk ◽  
Genetic Risk Factors ◽  
Hormone Receptor Status ◽  
Receptor Status

A detailed analysis of the risk factors influencing local and distant breast cancer recurrence during adjuvant endocrine therapy

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 562-562
Author(s):  
J. Houghton
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Tumor Size ◽  
Hormone Receptor ◽  
Distant Recurrence ◽  
Hormone Receptor Status ◽  
Nodal Involvement ◽  
Receptor Status ◽  
Factors Influencing ◽  
The Impact

562 Background: The ‘Arimidex’, Tamoxifen, Alone or in Combination (ATAC; ISRCTN18233230 ) trial compared the efficacy and safety of 5 years’ anastrozole, tamoxifen, or combination as adjuvant therapy for 9366 postmenopausal women with early invasive breast cancer. Here, risk factors influencing local and distant recurrences during the trial, independent of trial treatment, are assessed. Methods: The influence of standard baseline factors such as hormone receptor status, nodal involvement, tumor size, grade and age were evaluated on both local and distant recurrence rates. The use of other treatments (adjuvant chemotherapy, radiotherapy) and surgical status (mastectomy and axillary surgery) were also included. In addition, weight, body mass index, hysterectomy and prior hormone- replacement therapy were added. Cox models were used to analyze events by prognostic factors, and subsequently adjusted by country, before the production of confirmatory models. Results: For both local and distant recurrence, the highest risk correlated with poorer tumor differentiation, larger tumor size, increased nodal involvement and a negative hormone receptor status (see table ). While surgical status also affected the risk of developing a recurrence, previous treatments were less important, but residence in the USA showed a significant advantage. No association was seen with hysterectomy or weight for any recurrence. Conclusions: Although the pattern of risk varied for local and distant recurrence, tumor grade, size, and nodal involvement were the strongest risk factors for both. In comparison, the impact of previous treatments on hazard risk was lower. These data from a large international clinical trial confirm that women with less differentiated or larger tumors, and those with involved nodes, are at an increased risk of recurrence. [Table: see text] [Table: see text]

Hormone receptor status in primary breast cancer—time for a consensus?

2002 ◽  
Vol 38 (9) ◽  
pp. 1201-1203 ◽  
Author(s):  
G.C Wishart ◽  
M Gaston ◽  
A.A Poultsidis ◽  
A.D Purushotham
Keyword(s):  
Breast Cancer ◽  
Hormone Receptor ◽  
Primary Breast Cancer ◽  
Hormone Receptor Status ◽  
Receptor Status

A STUDY ON HORMONE RECEPTOR STATUS IN BREAST CANCER IN RELATION TO HISTOLOGICAL GRADING, AGE AND LYMPH NODE INVOLVEMENT

2021 ◽  
pp. 41-44
Author(s):  
R. Rani Suganya ◽  
M. Annapoorani ◽  
C. Naveen Kumar
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Hormone Receptor ◽  
Histological Grade ◽  
Systemic Disease ◽  
Lymph Node Involvement ◽  
Hormone Receptor Status ◽  
Major Health Problem ◽  
Receptor Status ◽  
Node Involvement

Breast cancer is the major health problem for the women throughout the world.Management of breast cancer has evolved to include both surgery for local disease and medical therapy for systemic disease. Multiple treatment options are available depending on various factors such as histological grade, hormone receptor status etc. The aim of this study is to correlate the hormone receptor status with prognostic factors such as lymph node involvement, tumour grading and age among patients diagnosed with breast cancer in our institution. The results of this study serve to prognosticate the severity of disease among various strata of patients.

scholarly journals Is the biology of breast cancer changing? A study of hormone receptor status 1984–86 and 1996–1997

2008 ◽  
Vol 34 (10) ◽  
pp. 1172
Author(s):  
Sylvia Brown ◽  
E. Mallon ◽  
J. Edwards ◽  
F. Campbell ◽  
L. McGlynn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hormone Receptor ◽  
Hormone Receptor Status ◽  
Receptor Status

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

Sign in / Sign up

Export Citation Format

Share Document