Dynamic Assessment of Systemic Inflammatory Markers in Predicting Pathological Complete Response After Neoadjuvant...

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype in which neoadjuvant chemotherapy plays an important role in initial treatment. Pathological complete response (pCR) following neoadjuvant therapy can serve as a surrogate marker for long-term survival. Our study aimed to evaluate the value of the Pan-Immune-Inflammation Value (PIV), Neutrophil-to-Lymphocyte Ratio (NLR), and Platelet-to-Lymphocyte Ratio (PLR), measured at multiple time points, in predicting pCR. Methods: We retrospectively included 89 patients with non-metastatic TNBC treated with neoadjuvant chemotherapy with or without immunotherapy, followed by surgery. Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Pan-Immune-Inflammation Value (PIV) were calculated at baseline, before the second treatment cycle, and at the pragmatic pre-transition assessment before a subsequent treatment phase, when applicable. The primary endpoint was pCR, defined as ypT0N0. Multivariable logistic regression models included age, Ki-67, clinical T stage, and tumor grade. Biomarker-extended models were compared with the clinical model on identical complete-case samples. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), likelihood ratio testing, calibration measures, Brier scores, and bootstrap internal validation with 2000 resamples. Results: pCR was achieved in 25 of 89 patients (28.1%). Baseline platelet counts were lower in patients with pCR than in those without pCR (median 246 × 10³/µL vs. 272 × 10³/µL; p = 0.021). At the pre-transition assessment, patients with pCR had lower monocyte counts (0.20 × 10³/µL vs. 0.57 × 10³/µL; p = 0.048) and lower PIVs (378.9 vs. 746.0; p = 0.011). Baseline PIV did not improve the clinical model. On the same 83-patient sample, adding baseline platelet count increased the apparent AUC from 0.751 to 0.807; however, the bootstrap 95% confidence interval (CI) for the AUC difference included zero (-0.003 to 0.128). On the same 75-patient sample, adding pre-transition PIV increased the apparent AUC from 0.735 to 0.798, with a bootstrap 95% confidence interval for the AUC difference of 0.005 to 0.142. The optimism-corrected AUC for the overall clinical model was 0.716, indicating lower internally validated performance than suggested by the apparent AUC. A smaller absolute increase in PIV from baseline to the pre-transition assessment was associated with pCR, but this finding was definition-dependent and remained exploratory. Conclusions: Standalone baseline markers have limited utility in predicting pCR in non-metastatic TNBC. Pre-transition PIV showed the most consistent incremental association with pCR beyond conventional clinical variables, whereas the added value of baseline platelet count was uncertain and baseline PIV provided no incremental benefit. Because of the retrospective design, limited sample size, treatment heterogeneity, and evidence of model optimism, these findings should be regarded as hypothesis-generating and require external validation.