Use of Retrospective Data to Forward-Simulate Cost-Efficient Pathways for Individual Patients Undergoing Open Versus Endovascular Repair of Non-Ruptured Abdominal Aortic Aneurysm

BACKGROUND: Young women with localized breast cancer represent a clinically distinct population with heterogeneous outcomes, yet age-specific prognostic models remain limited. Conventional risk stratification tools derived from mixed-age cohorts may fail to capture the complex interactions between tumor biology and treatment response in this group. METHODS: We conducted a single-center retrospective cohort study including 1,060 women aged ≤40 years diagnosed with stage I-III breast cancer between 2000 and 2023. Overall survival (OS) was analyzed using Kaplan-Meier estimates and multivariable Cox regression. To enable data-driven risk prediction beyond linear assumptions, a machine learning-based Random Survival Forest (RSF) model was developed to identify key prognostic features, quantify variable importance, and stratify patients into distinct risk groups. RESULTS: Among 1,060 eligible patients, 110 deaths (10.4%) occurred during a median follow-up of 79.8 months. Invasive pathological subtype (hazard ratio [HR] = 5.23, 95% confidence interval [CI] 1.18-23.22; CONCLUSION: By integrating clinicopathological variables with machine learning-based survival modeling, this study identified key prognostic factors associated with OS in young women with localized breast cancer. The findings highlight the prognostic importance of treatment-related factors and reveal an unexpected association between high Ki-67 expression and better survival in this population. These data-driven risk stratification approaches may contribute to more personalized prognostic assessment and warrant validation in prospective multicenter studies.