scholarly journals 906Can mammographic density add value to the Gail model in risk-stratifying women in BreastScreen Australia?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Louiza Velentzis ◽  
Pietro Procopio ◽  
Sarah Carr ◽  
Lisa Devereux ◽  
Bruce Mann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Risk Stratification ◽  
Mammographic Density ◽  
Invasive Breast Cancer ◽  
Odds Ratio ◽  
Assessment Tool ◽  
Questionnaire Data ◽  
Gail Model ◽  
Model Risk

Abstract Background There is significant interest in personalised, risk-based breast cancer screening. This requires high quality risk assessment. The ‘Gail model’ risk assessment tool has been validated on over 40,000 BreastScreen Australia participants. We assess whether adding mammographic density (MD) information improves risk stratification on that cohort. Methods We used questionnaire data, baseline MD readings (using AutoDensity) and linked screening and cancer registry records from 40,158 BreastScreen Australia participants aged 50–69 years (via the lifepool cohort). We investigated incident invasive breast cancer rates by quintiles of Gail model scores, MD, and combinations of Gail and MD. Results Gail scores and MD values were weakly correlated (r≤0.02). Gail and MD were each strong predictors of incident breast cancer, but stronger predictors when used in combination. For example, the odds ratio for incident invasive breast cancer was 3.6 (95%CI 2.5-6.3) for the 17% of women in the upper two quintiles of both Gail and MD scores compared to the 17% of women in the lower two quintiles of both scores. In comparison, the odds ratio for breast cancer between same-size (each 17%) upper and lower groups for Gail score alone was 2.5 (95%CI 1.8-3.4), and for MD 1.9 (95%CI 1.2-2.9). Conclusions Combining Gail and MD categories improves risk stratification on BreastScreen Australia participants, compared to using Gail or MD alone. Key messages While questionnaire data and MD measures are each strong predictors of future invasive breast cancer among BreastScreen Australia participants, risk prediction is stronger when questionnaire and MD measures are combined.

scholarly journals Prospective Validation of the NCI Breast Cancer Risk Assessment Tool and the Autodensity Mammographic Density Tool on 40,000 Australian Screening Program Participants

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 49s-49s ◽  
Author(s):  
C. Nickson ◽  
P. Procopio ◽  
L. Devereux ◽  
S. Carr ◽  
G. Mann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Density ◽  
Assessment Tool ◽  
Screening Program ◽  
Interval Cancer ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment

Background: In Australia and elsewhere, there is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires reliable, feasible and accurate estimates of risk. The US National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) and the AutoDensity fully automated mammographic density measurement tool have each been shown to stratify women into groups according to their risk of breast cancer; the AutoDensity tool also provides information on the likely sensitivity and specificity of mammographic screening tests. The Australian 'lifepool' cohort of over 53,000 women recruited predominantly through BreastScreen Australia screening program offers an opportunity to validate these tools and examine how they can be combined to estimate various risks. Aim: To validate BCRAT and AutoDensity on a large Australian population, and examine how the tools can be combined to provide information on breast cancer risk and the accuracy of the screening test. Methods: We use lifepool cohort questionnaire data and linked screening records and mammograms, cancer registrations and death records to describe the association between BCRAT and AutoDensity scores assessed at the time of screening and future breast cancer diagnosis. We use hazards models to account for censoring and describe outcomes according to mode of detection (screen-detected, interval cancers or other). Our primary analysis is restricted to women in the historical screening target age range of 50-69 with no prevalent breast cancer diagnosis on entry to the lifepool cohort. Results: The primary analysis included approximately 40,000 women with a median follow-up period of 4.5 years (1.1-6.5 years). The BCRAT tool generated a median 5-year breast cancer risk score of 1.5% (range 0.6%-22.0%). Compared with women in the lowest quintile of this score, women in the highest quintile had a 2.3-fold risk (95% CI 1.7-3.0, P < 0.001) of incident invasive breast cancer. For the approximately 35,000 women with digital screening mammograms on enrolment, women in the highest quintile of AutoDensity values had a 1.5-fold risk (95% CI 1.1-2.0 P = 0.011) of incident invasive breast cancer and a 2.6-fold risk (95% CI 1.1-6.2, P = 0.034) of an interval cancer compared with women in the lowest quintile. With BRCAT and AutoDensity measurements weakly correlated (r2= 0.003, P = 0.05), we demonstrate various approaches to combining this information to stratify women according to breast cancer risk and risk of an interval cancer. Conclusion: The US National Cancer Institute Breast Cancer Risk Assessment Tool and the AutoDensity mammographic density tool can be used to stratify breast cancer screening participants into risk groups according to their future breast cancer risk and the risk of an interval cancer. This is likely to be of interest to screening program managers and policy-makers, and women considering screening participation.

Identifying women at high risk for invasive breast cancer at the time of screening mammography.

2013 ◽  
Vol 31 (26_suppl) ◽  
pp. 6-6
Author(s):  
Mary Lorraine Lopresti ◽  
Kathryn L. Edmiston ◽  
Rahul Sood ◽  
Shrinkala Khanna
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
High Risk ◽  
Invasive Breast Cancer ◽  
Screening Mammography ◽  
Environmental Data ◽  
Gail Model ◽  
Model Risk ◽  
Increased Risk ◽  
History Of

6 Background: Most mammography centers collect reproductive, family, and environmental data on patients presenting for screening mammograms. These data, if entered into a Gail Model Risk Assessment Tool, can help identify those patients at increased risk. These patients can then be referred to high-risk centers that will focus on risk-reduction strategies. Methods: This study is an observational prospective cohort of 1,000 women presenting for mammographic screening or diagnostic evaluation at mammography clinics affiliated with a single institution. Women at the time of their mammogram were asked to fill out a standard intake sheet by the institutional clinic. These data sheets were gathered and Gail model risk scores were calculated. Women with a 5-year risk of invasive breast cancer of 1.7% or greater were identified. Patients with a history of breast cancer or who underwent diagnostic mammography were excluded. A high risk subgroup was identified and risk factors were analyzed. Women at particularly high risk ( ≥ 3.4% or double the 5-year risk) were analyzed separately. Results: Of 1,000 women screened, 366 had ≥ 1.7% 5-year risk of invasive breast cancer. 26% (96) of these women were under 60 years old while 74% (270) were ≥ 60 years old. Among the latter group, 19.6% (53) were found to have ≥ 3.4% of developing invasive cancer. In these women with double the 5-year risk, 96.2% had a family history of breast cancer and 92.4% had a prior biopsy. Similarly, in women under 60, greater than half were high risk secondary to a prior biopsy or family history. Conclusions: 1/3 of patients who receive annual screening are at high risk for breast cancer. These patients can be identified from data routinely obtained at the time of screening mammography. Many were found to be at increased risk due to a strong family history or prior biopsy. Mammography centers may be the ideal setting in which to alert these patients of their risk and refer them to high risk centers for genetic counseling and consideration of chemoprevention.

scholarly journals Assessing Breast Cancer Risk Estimates Based on the Gail Model and Its Predictors in Qatari Women

2017 ◽  
Vol 8 (3) ◽  
pp. 180-187 ◽  
Author(s):  
Abdulbari Bener ◽  
Funda Çatan ◽  
Hanadi R. El Ayoubi ◽  
Ahmet Acar ◽  
Wanis H. Ibrahim
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Arab Women ◽  
Gail Model ◽  
Breast Cancer Risk Assessment

Background: The Gail model is the most widely used breast cancer risk assessment tool. An accurate assessment of individual’s breast cancer risk is very important for prevention of the disease and for the health care providers to make decision on taking chemoprevention for high-risk women in clinical practice in Qatar. Aim: To assess the breast cancer risk among Arab women population in Qatar using the Gail model and provide a global comparison of risk assessment. Subjects and Methods: In this cross-sectional study of 1488 women (aged 35 years and older), we used the Gail Risk Assessment Tool to assess the risk of developing breast cancer. Sociodemographic features such as age, lifestyle habits, body mass index, breast-feeding duration, consanguinity among parents, and family history of breast cancer were considered as possible risks. Results: The mean age of the study population was 47.8 ± 10.8 years. Qatari women and Arab women constituted 64.7% and 35.3% of the study population, respectively. The mean 5-year and lifetime breast cancer risks were 1.12 ± 0.52 and 10.57 ± 3.1, respectively. Consanguineous marriage among parents was seen in 30.6% of participants. We found a relationship between the 5-year and lifetime risks of breast cancer and variables such as age, age at menarche, gravidity, parity, body mass index, family history of cancer, menopause age, occupation, and level of education. The linear regression analysis identified the predictors for breast cancer in women such as age, age at menarche, age of first birth, family history and age of menopausal were considered the strong predictors and significant contributing risk factors for breast cancer after adjusting for ethnicity, parity and other variables. Conclusion: The current study is the first to evaluate the performance of the Gail model for Arab women population in the Gulf Cooperation Council. Gail model is an appropriate breast cancer risk assessment tool for female population in Qatar.

scholarly journals Risk assessment in precapillary pulmonary hypertension: a comparative analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Thomas Sonnweber ◽  
Eva-Maria Schneider ◽  
Manfred Nairz ◽  
Igor Theurl ◽  
Günter Weiss ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Pulmonary Hypertension ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Assessment Tool ◽  
Risk Models ◽  
Non Invasive

Abstract Background Risk stratification is essential to assess mortality risk and guide treatment in patients with precapillary pulmonary hypertension (PH). We herein compared the accuracy of different currently used PH risk stratification tools and evaluated the significance of particular risk parameters. Methods We conducted a retrospective longitudinal observational cohort study evaluating seven different risk assessment approaches according to the current PH guidelines. A comprehensive assessment including multi-parametric risk stratification was performed at baseline and 4 yearly follow-up time-points. Multi-step Cox hazard analysis was used to analyse and refine risk prediction. Results Various available risk models effectively predicted mortality in patients with precapillary pulmonary hypertension. Right-heart catheter parameters were not essential for risk prediction. Contrary, non-invasive follow-up re-evaluations significantly improved the accuracy of risk estimations. A lack of accuracy of various risk models was found in the intermediate- and high-risk classes. For these patients, an additional evaluation step including assessment of age and right atrium area improved risk prediction significantly. Discussion Currently used abbreviated versions of the ESC/ERS risk assessment tool, as well as the REVEAL 2.0 and REVEAL Lite 2 based risk stratification, lack accuracy to predict mortality in intermediate- and high-risk precapillary pulmonary hypertension patients. An expanded non-invasive evaluation improves mortality risk prediction in these individuals.

Gail Model Risk Factors: Impact of Adding an Extended Family History for Breast Cancer

2008 ◽  
Vol 14 (3) ◽  
pp. 221-227 ◽  
Author(s):  
Anna Crispo ◽  
Giuseppe D’Aiuto ◽  
MariaRosaria De Marco ◽  
Massimo Rinaldo ◽  
Maria Grimaldi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Family History ◽  
Extended Family ◽  
Gail Model ◽  
Model Risk

scholarly journals Research-based PAM50 signature and long-term breast cancer survival

2019 ◽  
Vol 179 (1) ◽  
pp. 197-206 ◽  
Author(s):  
Minya Pu ◽  
Karen Messer ◽  
Sherri R. Davies ◽  
Tammi L. Vickery ◽  
Emily Pittman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Cancer Survival ◽  
Assessment Tool ◽  
Menopausal Status ◽  
Postmenopausal Breast Cancer ◽  
Breast Cancer Survival ◽  
Gene Signatures ◽  
Hypoxia Signature

Abstract Purpose Multi-gene signatures provide biological insight and risk stratification in breast cancer. Intrinsic molecular subtypes defined by mRNA expression of 50 genes (PAM50) are prognostic in hormone-receptor positive postmenopausal breast cancer. Yet, for 25–40% in the PAM50 intermediate risk group, long-term risk remains uncertain. Our study aimed to (i) test the long-term prognostic value of the PAM50 signature in pre- and post-menopausal breast cancer; (ii) investigate if the PAM50 model could be improved by addition of other mRNAs implicated in oncogenesis. Methods We used archived FFPE samples from 1723 breast cancer survivors; high quality reads were obtained on 1253 samples. Transcript expression was quantified using a custom codeset with probes for > 100 targets. Cox models assessed gene signatures for breast cancer relapse and survival. Results Over 15 + years of follow-up, PAM50 subtypes were (P < 0.01) associated with breast cancer outcomes after accounting for tumor stage, grade and age at diagnosis. Results did not differ by menopausal status at diagnosis. Women with Luminal B (versus Luminal A) subtype had a > 60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with > 40% higher hazard in the highest vs lowest hypoxia tertiles. Conclusions PAM50 intrinsic subtypes were independently prognostic for long-term breast cancer survival, irrespective of menopausal status. Addition of hypoxia signatures improved risk prediction. If replicated, incorporating the 13-gene hypoxia signature into the existing PAM50 risk assessment tool, may refine risk stratification and further clarify treatment for breast cancer.

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