Integrating machine learning and clinicopathological data to stratify survival risk in young women with localized...

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.

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.

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; p = 0.030), nipple invasion (HR = 3.95, 95% CI 2.14-7.27; p < 0.001), and advanced T stage (T2-4 vs. Tis/T1; HR = 1.60, 95% CI 1.03-2.48; p = 0.036) were independently associated with worse OS. By contrast, receipt of endocrine therapy (HR = 0.54, 95% CI 0.36-0.80; p = 0.002) and radiotherapy (HR = 0.53, 95% CI 0.32-0.86; p = 0.010) were associated with better OS. Notably, high Ki67 expression (≥35%; HR = 0.39, 95% CI 0.21-0.71; p = 0.002) was associated with improved OS. The RSF model confirmed these predictors, ranked radiotherapy as the most influential variable, and provided effective risk stratification (C-index = 0.723).

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.