Critical Diagnostic Delay Thresholds in Breast Cancer: A Molecular Subtype-Based Causal Analysis From Saudi Arabia.

Introduction Diagnostic delays in breast cancer can significantly affect treatment outcomes. Currently, the causal mechanisms and critical time thresholds remain poorly defined across the different molecular subtypes of breast cancer. We investigated the relationship between diagnostic delays and breast cancer outcomes based on the data from our center, with a focus on identifying actionable intervention points within the diagnostic pathway. Methods We conducted a retrospective cohort study of 802 breast cancer patients treated at King Fahad Specialist Hospital in Qassim Province, Saudi Arabia (2017-2024). Using directed acyclic graphs and mediation analysis, we quantified the causal pathways through which delays impact the outcomes. Markov chain modeling was utilized to determine the molecular subtype-specific critical thresholds where stage migration probability exceeds 10%. Results We found that 589 patients (73.5%) experienced high-risk delays (over two months). Stage migration emerged as the primary mediator, accounting for 67.3% (95% CI: 58.4-76.1%) of the total effect of delays on survival. We have identified multiple critical thresholds across molecular subtypes: 38 days for triple-negative, 52 days for HER2-positive, and 85 days for ER+/PR+/HER2- tumors. Hazard ratios for mortality increased progressively with delay duration, from 1.18 (95% CI: 1.05-1.32) for delays of two weeks to one month to 2.35 (95% CI: 2.06-2.67) for delays that are equal to or more than one year, translating to an average 3.40 life years lost per patient. Conclusions The impact of diagnostic delays on breast cancer outcomes is fundamentally governed by tumor biology, with significant vulnerability thresholds aligned with molecular aggressiveness. Our findings support applying a biologically informed triage system where molecular characteristics determine maximum acceptable diagnostic intervals. Using the suggested approach, we may achieve a better balance in the resource constraints with biological imperatives, and possibly improve survival outcomes without proportional increases in healthcare expenditure.