The impact of timely treatment on breast cancer‐specific survival may differ by tumor stage. We aim to study the impact of delayed first treatment on overall survival across different tumor stages. In addition, we studied the impact of delayed adjuvant treatments on survival in patients with invasive nonmetastatic breast cancer who had surgery ≤90 days postdiagnosis. This population‐based study includes 11 175 breast cancer patients, of whom, 2318 (20.7%) died (median overall survival = 7.9 years). To study the impact of delayed treatment on survival, hazard ratios and corresponding 95% confidence intervals were estimated using Cox proportional‐hazards models. The highest proportion of delayed first treatment (>30 days postdiagnosis) was in patients with noninvasive breast cancer (61%), followed by metastatic breast cancer (50%) and invasive nonmetastatic breast cancer (22%). Delayed first treatment (>90 vs ≤30 days postdiagnosis) was associated with worse overall survival in patients with invasive nonmetastatic (HR: 2.25, 95% CI 1.55‐3.28) and metastatic (HR: 2.09, 95% CI 1.66‐2.64) breast cancer. Delayed adjuvant treatment (>90 vs 31‐60 days postsurgery) was associated with worse survival in patients with invasive nonmetastatic (HR: 1.50, 95% CI 1.29‐1.74). Results for the Cox proportional‐hazards models were similar for breast cancer‐specific death. A longer time to first treatment (31‐90 days postdiagnosis) may be viable for more extensive diagnostic workup and patient‐doctor decision‐making process, without compromising survival. However, patients’ preference and anxiety status need to be considered.

Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model’s performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580–0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86–1.71; E/Oshort-term ranges:1.24–3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.