scholarly journals Breast cancer risk stratification for mammographic screening: A nation‐wide screening cohort of 24,431 women in Singapore

2021 ◽  
Author(s):  
Peh Joo Ho ◽  
Fuh Yong Wong ◽  
Wen Yee Chay ◽  
Elaine Hsuen Lim ◽  
Zi Lin Lim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Mammographic Screening

scholarly journals Health professionals’ perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease

2019 ◽  
Vol 40 (1) ◽  
Author(s):  
Svetlana Puzhko ◽  
Justin Gagnon ◽  
Jacques Simard ◽  
Bartha Maria Knoppers ◽  
Sophia Siedlikowski ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Health Professionals

scholarly journals Uptake of Risk Appropriate Behaviors After Breast Cancer Risk Stratification in the Mammography Screening Population

2020 ◽  
Vol 17 (10) ◽  
pp. 1285-1288
Author(s):  
Claire C. Conley ◽  
Bethany L. Niell ◽  
Bianca M. Augusto ◽  
McKenzie McIntyre ◽  
Richard Roetzheim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Mammography Screening ◽  
Screening Population

Breast cancer risk and participation in mammographic screening

Maturitas ◽  
1990 ◽  
Vol 12 (2) ◽  
pp. 156
Author(s):  
S. Taplin ◽  
C. Anderman ◽  
L. Grothaus
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Screening

scholarly journals Epigenetic Testing for Breast Cancer Risk Stratification

2012 ◽  
Author(s):  
David Euhus
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification

scholarly journals Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

2019 ◽  
Vol 112 (3) ◽  
pp. 278-285 ◽  
Author(s):  
Parichoy Pal Choudhury ◽  
Amber N Wilcox ◽  
Mark N Brook ◽  
Yan Zhang ◽  
Thomas Ahearn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Hispanic Women ◽  
Target Population ◽  
Mammographic Breast Density ◽  
Polygenic Risk Score ◽  
Breast Cancer Risk Assessment

Abstract Background External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

Multi-institutional comparison of breast cancer risk stratification by 70-gene signature and 21-gene assay.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 522-522 ◽  
Author(s):  
David J. Dabbs ◽  
Charles E. Cox ◽  
Steven Shivers ◽  
Nathaniel Bouganim ◽  
Jamil Asselah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Cleveland Clinic ◽  
Gene Signature ◽  
Risk Category ◽  
Gene Assay ◽  
Mcgill University

522 Background: Breast cancer risk stratification with the 70-gene signature (70-GS) provides a binary low risk (LR) or high risk (HR) result; by contrast the 21-gene assay (21-GA) provides LR, intermediate (IR), and HR results. Results from these two assays were compared for 769 patients from 5 institutions. Methods: The study included patients from McGill University (n = 86), UPMC (n = 437), USF (n = 135), Morton Plant North Bay Hospital (n = 79), and Cleveland Clinic (n = 32, all 21-GA IR). Results: With the 70-GS, 487 (63%) patients had a LR and 282 (37%) patients had a HR result. Excluding 32 cases selected for 21-GA IR results (n = 737), the 21-GA gave 369 (50%), 250 (34%), and 118 (16%) patients with LR, IR, and HR scores, respectively. Using the TAILORx cutoff, there were 134 (18%), 432 (59%), and 171 (23%) patients with LR, IR, and HR scores, respectively. There were 329 (45%) and 486 (66%) patients who were not classified in the same risk category by both assays using the clinical and TAILORx cutoffs for IR, respectively. Conclusions: In a large multi-institutional study the 70-GS and 21-GA results were discordant in 45-66% of patients, and the proportion of patients with a 21-GA score in the IR range varied from 34-59%. The 70-GS provided clinically actionable results for all patients. [Table: see text]

scholarly journals Breast cancer risk and participation in mammographic screening.

1989 ◽  
Vol 79 (11) ◽  
pp. 1494-1498 ◽  
Author(s):  
S Taplin ◽  
C Anderman ◽  
L Grothaus
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Screening

Breast cancer risk prediction model: a nomogram based on common mammographic screening findings

2013 ◽  
Vol 23 (9) ◽  
pp. 2413-2419 ◽  
Author(s):  
J. M. H. Timmers ◽  
A. L. M. Verbeek ◽  
J. IntHout ◽  
R. M. Pijnappel ◽  
M. J. M. Broeders ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Mammographic Screening ◽  
Risk Prediction Model

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

2019 ◽  
Vol 26 (4) ◽  
pp. 204-211
Author(s):  
Ellie Darcey ◽  
Ravi Ambati ◽  
Helen Lund ◽  
Andrew Redfern ◽  
Christobel Saunders ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Strong Predictor ◽  
Mammographic Screening ◽  
Routine Screening ◽  
Mammographic Breast Density ◽  
Trial Participation ◽  
Participation Rates ◽  
Post Menopausal

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.

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