Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35–45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: −0.16, 0.28). There was little association with dense area (between-women r = −0.12, 95%CI: −0.38, 0.16; within-women r = 0.01, 95%CI: −0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: −0.31 (95%CI: −0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size.

Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review.

Background: Breast cancer screening recommendations vary around the world, but most are based on age or inherited genetic risk factors. For instance, the American Cancer Society recommends annual mammography plus breast MRI starting at age 30yr for women at high risk of breast cancer based mainly on family history or high-risk genes. Women at average risk (no strong family history or high-risk genes) are recommended to have the option of annual mammography starting at age 40yr. Risk-based screening, which aims to personalise screening to an individual woman’s risk of breast cancer based on a more comprehensive risk assessment than just age, family history, or high-risk genes, might improve current screening strategies. Methods: We developed a deterministic model to estimate the incidence of advanced (node-positive) breast cancer (plus number of screens) for different risk-based screening strategies in a UK setting. The proportion of screen-detected and interval cancers was estimated for various screening intervals using a model developed by Launoy et al. and parameters for sensitivity (0.92) and annual transition rate from asymptomatic to symptomatic disease (0.25) from The Swedish Two-County Trial. The proportion of node-positive cancers was estimated for screen-detected (22%) and interval (53%) cancers, using data from the NHS Breast Screening Programme (England, 2015-18, women aged 47yr+). Choice of mammography screening regimen was based on Tyrer-Cuzick 10yr risk (v8 including age, family history, reproductive factors, benign breast disease, SNPs and breast density). The proportion of women in each risk group was estimated from a UK cohort study investigating breast cancer risk at screening (PROCAS). In a hypothetical cohort of 3.45M women, 1M women would be identified as either high-risk (>8% 10yr risk; n=241,379) or low-risk (< 1.4% 10yr risk; n=758,621). In these 1M high/low-risk women, we evaluated two risk-based screening scenarios, comparing their effects with usual triennial screening starting at age 50yr (which was proposed for the 2.45M women at intermediate-risk (1.4-8% 10yr risk)). Scenario (1): Changing screening interval based on risk (high-risk every 1yr; low-risk every 5yr) for screening between 50-70yr. Scenario (2): Changing the starting age of screening based on risk (high-risk start annual screening at 45yr followed by triennial screening from 50yr; low-risk start triennial screening at 55yr); follow-up 45-55yr. We assessed the trade-off between the decreased/increased number of node-positive breast cancers and increased/decreased number of screens with the high/low-risk regimens, respectively. A sensitivity analysis considered risk stratification without breast density. Results: Scenario (1): Changing screening interval based on risk reduced the number of node-positive cancers in high-risk women by 2,194 (with 3.14M additional mammograms) and increased the number of node-positive cancers in low-risk women by 910 (with 2.28M fewer mammograms) when compared with usual screening; a difference of 1,284 fewer node-positive cancers and 862,069 additional screens. Scenario (2): Additional annual mammograms for high-risk women at 45-49yr reduced the number of node-positive cancers by 1,392 (with 1.21M additional mammograms); starting triennial screening at 55yr rather than 50yr for low-risk women increased the number of node-positive cancers by 841 (with 1.52M fewer mammograms); a difference of 551 fewer node-positive cancers and 310,345 fewer screens. Excluding breast density from risk assessment reduced the number identified as high or low-risk, and thus the number of advanced cancers prevented and screens required, but the overall findings were unchanged. Conclusion: Changing the starting age of screening based on risk of breast cancer is likely to be more effective per screen required at reducing the rate of advanced breast cancer than changing the screening interval based on risk. Table 1: Results for Scenario (1) Risk-based screening (changing screening interval based on risk: high-risk every 1 year; low-risk every 5 years) versus usual screening (every 3 years) between age 50-70 years (plus an additional 3 years of follow-up to adjust for the effect of screening on risk of breast cancer). N: Number; %: percentage; node+: Node-positive breast cancer; Δ: Difference; yr: Year; N/A: not applicable. Table 2: Results for Scenario (2) Risk-based screening (changing the starting age of screening based on risk: high-risk start annual screening at age 45-49 years followed by triennial screening from age 50 years; low-risk start triennial screening at age 55 years) versus usual screening (triennial screening starting at age 50 years), with follow-up from age 45-55 years. n: Number; 1M: 1 million; node+: Node-positive breast cancer; Δ: Difference; yr: Year. Table 3: Results for sensitivity analysis - Scenarios (1) and (2) with risk assessment including/excluding breast density Scenario (1): Risk-based screening (changing screening interval based on risk: high-risk every 1 year; low-risk every 5 years) versus usual screening (every 3 years) between age 50-70 years (plus an additional 3 years of follow-up to adjust for the effect of screening on risk of breast cancer). Scenario (2): Risk-based screening (changing the starting age of screening based on risk: high-risk start annual screening at age 45-49 years followed by triennial screening from age 50 years; low-risk start triennial screening at age 55 years) versus usual screening (triennial screening starting at age 50 years), with follow-up from age 45-55 years. n: Number; node+: Node-positive breast cancer; Δ: Difference; yr: Year. Citation Format: Emma C. Atakpa, Jack Cuzick, Stephen W. Duffy, D. Gareth Evans, Sacha J. Howell, Adam R. Brentnall. PD14-01 A model to assess the utility of risk-based screening algorithms [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD14-01.

Background: This study aimed to assess the impact of multiple COVID-19 waves on UK gynaecological-oncology services. Methods: An online survey was distributed to all UK-British-Gynaecological-Cancer-Society members during three COVID-19 waves from 2020 to2022. Results: In total, 51 hospitals (including 32 cancer centres) responded to Survey 1, 42 hospitals (29 centres) to Survey 2, and 39 hospitals (30 centres) to Survey 3. During the first wave, urgent referrals reportedly fell by a median of 50% (IQR = 25–70%). In total, 49% hospitals reported reduced staffing, and the greatest was noted for trainee doctors, by a median of 40%. Theatre capacity was reduced by a median of 40%. A median of 30% of planned operations was postponed. Multidisciplinary meetings were completely virtual in 39% and mixed in 65% of the total. A median of 75% of outpatient consultations were remote. By the second wave, fewer hospitals reported staffing reductions, and there was a return to pre-pandemic urgent referrals and multidisciplinary workloads. Theatre capacity was reduced by a median of 10%, with 5% of operations postponed. The third wave demonstrated worsening staff reductions similar to Wave 1, primarily from sickness. Pre-pandemic levels of urgent referrals/workload continued, with little reduction in surgical capacity. Conclusion: COVID-19 led to a significant disruption of gynaecological-cancer care across the UK, including reduced staffing, urgent referrals, theatre capacity, and working practice changes. Whilst disruption eased and referrals/workloads returned to normal, significant staff shortages remained in 2022, highlighting persistent capacity constraints.