Strategies for adding genetic testing and yield of reclassification in terms of fraction of women tested and fraction of women reclassified.

<p>Strategies for adding genetic testing and yield of reclassification in terms of fraction of women tested and fraction of women reclassified.</p

Reclassification of risk with genetic testing.

<p>Percentages in each cell represent the percentage of the total population. Sections shaded in lighter gray and darker gray represent the group of women reclassified above and below the threshold for chemoprevention, respectively.</p

Distribution of risk in the BCSC pre- and post-genetic testing.

<p>The X-axis described the 5-year calculated probabilities for breast cancer. The Y-axis describes the fraction of the population at each interval of risk for women using just the BCSC model (yellow bars) and the BCSC model with SNPs (red bars.) The last vertical bar represents the fraction of the population with ≥ 6% risk of breast cancer in the next 5 years.</p

Proportion of each risk group with >3% probability of breast cancer before and after SNP testing.

<p>Proportion of each risk group with >3% probability of breast cancer before and after SNP testing.</p

Percent of total benefit derived by SNP testing as a function of testing different percent of population.

<p>The X-axis represents the percent of the population tested in each scenario. The Y-axis represents the percent of the benefit derived from testing for reclassification of women. The values on the Y-axis are derived from each scenario in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168601#pone.0168601.t003" target="_blank">Table 3</a>. Light gray lines with squares represents the percent of reclassification that occurs to below the treatment threshold out of the total that can be reclassified if everyone is tested. Medium gray line with diamonds represents the percent of reclassification that occurs to above the treatment threshold. The black gray line with triangles represents the percent of any reclassification (above or below the treatment threshold) out of the total possible reclassification if everyone were tested. For example in scenario 1, 9.2% of women are tested (X-axis) and 1.7% out of a possible 5.5% (30.6%) are reclassified above the treatment threshold (first medium diamond), 2.1% out of a possible 2.7% (75.5%) are reclassified below the treatment threshold (light gray square), and 3.8% out of a total possible 8.2% (45.5%) are reclassified either above or below the treatment threshold (black triangle).</p

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

<div><p>Purpose</p><p>Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.</p><p>Methods</p><p>Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (<1%; 1–1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1–13 years; median: 5.4–5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35–44; 45–54; 55–69; 70–89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.</p><p>Results</p><p>Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75–0.90); BCRAT: HR = 0.72 (95% CI = 0.65–0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75–0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55–69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35–44 years.</p><p>Conclusions</p><p>Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively.</p></div

Predicted risk of developing breast cancer versus risk of death (age-stratified, adjusted models).

<p>Age-stratified models, adjusted for age in single years, registry, year of diagnosis, mode of detection, AJCC stage, treatment (surgery and chemotherapy: yes/no) and income (zip code of residence)</p

Characteristics of 37,939 Women with Breast Cancer.

<p>Characteristics of 37,939 Women with Breast Cancer.</p

Predicted 5-year risk of developing breast cancer versus clinicopathologic characteristics.

<p>Predicted 5-year risk of developing breast cancer versus clinicopathologic characteristics.</p

Best linear regression fit line with 95% confidence interval bands for percentage fibroglandular density (top), log fibroglandular volume (middle), and total breast volume (bottom) for MRI versus either SXA (left), Quantra (center), or Volpara (right) measures.

<p>Solid points correspond to example images in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081653#pone-0081653-g004" target="_blank">Figure 4</a>.</p