Quantifying the impact of treatment delays on breast cancer survival outcomes: a comprehensive meta-analysis.

Abstract

Treatment delay in breast cancer care represents a significant concern in oncology, potentially impacting patient survival outcomes. While various factors can contribute to delayed treatment initiation, the quantitative relationship between specific delay intervals and survival remains incompletely understood in breast cancer management. Our study aims to explore the impact of treatment delays on survival outcomes in breast cancer. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases, covering publications from 2000 to 2025. From an initial 6222 records, 18 eligible studies comprising 25 cohorts were included. Hazard ratios (HRs) for all-cause and breast cancer-specific mortality were extracted or calculated for treatment delays of 4, 8, and 12 weeks. Random-effects meta-analyses were performed, and heterogeneity and publication bias were assessed using I² statistics, funnel plots, and Egger's test. This meta-analysis revealed progressively increasing mortality risks with longer treatment delays. For all-cause mortality, HRs increased from 1.12 (95% CI 1.08-1.15) at 4 weeks to 1.25 (95% CI 1.17-1.33) at 8 weeks, and 1.39 (95% CI 1.26-1.53) at 12 weeks. Breast cancer-specific mortality showed more pronounced effects, with HRs of 1.20 (95% CI 1.06-1.36), 1.43 (95% CI 1.11-1.84), and 1.71 (95% CI 1.18-2.49) for 4-, 8-, and 12-week delays, respectively. Analyses combining both survival outcomes demonstrated consistent risk elevation across all time intervals (4 weeks: HR = 1.12, 95% CI 1.09-1.16; 8 weeks: HR = 1.26, 95% CI 1.18-1.34; 12 weeks: HR = 1.41, 95% CI 1.29-1.55). While heterogeneity was significant (I² = 54-92%), no substantial publication bias was detected. Delays in initiating breast cancer treatment are associated with significantly worse survival, particularly for cancer-specific mortality. Each additional 4-week delay increases the hazard of death by over 10%, underscoring the urgency of minimizing delays in diagnosis-to-treatment pathways. These findings have critical implications for healthcare systems, clinical decision-making, and public health policy.