ArticleFrontiers in medicine2026
Integrating machine learning and clinicopathological data to stratify survival risk in young women with localized breast cancer.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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