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.

Keywords: All-cause mortality; Breast cancer–specific mortality; Cancer prognosis; Hazard ratio; Mortality risk; Survival outcomes; Treatment delay.

Fig. 1

Flow diagram depicting the study selection process

Fig. 2

Impact of a 4-week treatment delay on mortality in breast cancer patients. The upper panel depicts all-cause mortality, while the lower panel presents breast cancer–specific mortality. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using a random-effects model. Each square represents an individual study estimate, with the size proportional to its statistical weight, while horizontal lines indicate the corresponding CIs. The black diamond represents the pooled effect estimate. Heterogeneity across studies is quantified using the I² statistic for both subgroups and for all studies. Abbreviations: CI, confidence interval; HR, hazard ratio; IV, inverse variance; SE, standard error

Fig. 3

Association between an 8-week treatment delay and mortality in breast cancer patients. Abbreviations: CI, confidence interval; HR, hazard ratio; IV, inverse variance; SE, standard error

Fig. 4

Effect of a 12-week treatment delay on mortality in breast cancer patients. The upper panel shows all-cause mortality, with a highly significant pooled hazard ratio of 1.39 (95% CI 1.26–1.53). The lower panel presents breast cancer–specific mortality, with a pooled HR of 1.71 (95% CI 1.18–2.49). Abbreviations: CI, confidence interval; HR, hazard ratio; IV, inverse variance; SE, standard error

Fig. 5

Funnel plots assessing publication bias in the meta-analysis of the effect of treatment delay on mortality in breast cancer patients. A Analysis of all-cause mortality across 20 studies; B analysis of breast cancer–specific mortality in five studies; C combined analysis of both survival outcomes across all 25 studies. The x-axis represents hazard ratios, while the y-axis shows the standard error. The vertical red line indicates no effect (HR = 1.0), and the dotted lines represent the 95% confidence intervals