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.