Machine learning-driven identification and immunohistochemical validation of an integrated immune-inflammatory phenotype for disease-free survival stratification in breast cancer

BackgroundRecurrence risk in breast cancer remains heterogeneous, and conventional clinicopathological variables may not fully capture the contribution of immune-related factors. We compared multiple survival modeling strategies and evaluated whether an integrated immune-inflammatory phenotype could improve disease-free survival (DFS) stratification.MethodsThis retrospective single-center study included 503 patients with surgically treated breast cancer between January 2020 and December 2025.

Stromal tumor-infiltrating lymphocytes (TILs) were assessed from pathological sections, and the systemic immune-inflammation index (SII) was calculated from pre-treatment blood counts. An integrated immune phenotype was defined as favorable (high TILs/low SII), poor (low TILs/high SII), or intermediate (all remaining combinations).

A base clinical Cox model, an immune-extended Cox model, LASSO-Cox, CoxBoost, and random survival forest (RSF) were compared using C-index, time-dependent area under the curve (AUC), integrated Brier score (IBS), and decision curve analysis. Conventional survival analyses, restricted cubic spline analysis, and immunohistochemical validation with CD8 and CD163 staining were also performed.ResultsDuring follow-up, 107 patients (21.3%) experienced a DFS event.