To build a predictive scoring model based on simple immune and inflammatory parameters to predict postoperative survival in patients with breast cancer.
We used a brand-new immuno-inflammatory index—pan-immune-inflammation value (PIV)—to retrospectively evaluate the relationship between PIV and overall survival (OS), and based on the results of Cox regression analysis, we established a simple scoring prediction model based on several independent prognostic parameters. The predictive accuracy of the model was evaluated and independently validated.
A total of 1,312 patients were included for analysis. PIV was calculated as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). According to the best cutoff value of PIV, we divided the patients into two different subgroups, high PIV (PIV > 310.2) and low PIV (PIV ≤ 310.2), associated with significantly different survival outcomes (3-year OS, 80.26% vs. 86.29%, respectively; 5-year OS, 62.5% vs. 71.55%, respectively). Six independent prognostic factors were identified and used to build the scoring system, which performed well with a concordance index (C-index) of 0.759 (95% CI: 0.715–0.802); the calibration plot showed good calibration.
We have established and verified a simple scoring system for predicting prognosis, which can predict the survival of patients with operable breast cancer. This system can help clinicians implement targeted and individualized treatment strategies.