The role of pan-immune-inflammation index in the prognosis of Chinese cases with triple-negative breast cancer following surgical resection.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer associated with high recurrence rates and poor survival outcomes. Growing evidence suggests that systemic inflammation plays a critical role in tumor progression and immune evasion. The pan-immune-inflammation value (PIV), a composite index derived from peripheral blood counts, has emerged as a potential biomarker of host immune and inflammatory status. Objective: This study aimed to evaluate the prognostic value of preoperative PIV in Chinese cases with TNBC following curative surgical resection. Methods: We conducted a retrospective cohort study of 312 TNBC cases treated at a tertiary center in China between January 2015 and March 2020. PIV was calculated as (neutrophil count × platelet count × monocyte count)/lymphocyte count using preoperative blood tests. According to a ROC-derived cutoff value of 353, cases were stratified into low and high PIV groups. Kaplan-Meier curves and Cox regression analyses were used to analyze survival outcomes, like disease-free survival (DFS) and overall survival (OS). Confounders for multivariate adjustment were selected based on clinical relevance and univariate significance (p < 0.10). Model performance was evaluated using Harrell's concordance index (C-index). Results: Cases with a high PIV showed significantly worse survival outcomes. The 5-year OS was 62.5% in the high PIV group compared with 71.6% in the low PIV group. High PIV was also associated with shorter DFS (median 36.8 vs. 45.2 months, p < 0.05). Multivariate analysis confirmed high PIV as an independent predictor of poor OS (HR, 1.75; p = 0.003) and DFS (HR, 1.61; p = 0.009), even after adjusting for tumor stage, nodal status, and histologic grade. Conclusion: Preoperative PIV is an independent and accessible prognostic biomarker in Chinese cases with TNBC following surgery. Its integration into clinical risk models may aid in identifying high-risk cases and tailoring postoperative management strategies for them.