Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer.

Abstract

Background: Pathological complete response (pCR) is an important indicator for evaluating the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. The role of systemic inflammation markers in predicting pCR and the long-term prognosis of breast cancer patients undergoing NAC remains controversial. The purpose of this study was to explore the potential predictive and prognostic value of systemic inflammation markers (NLR, PLR, LMR, NMR) and clinicopathological characteristics in breast cancer patients receiving NAC and construct a pCR prediction model based on these indicators.

Methods: A retrospective analysis was conducted on 209 breast cancer patients who received NAC at Nanjing Drum Tower Hospital between January 2010 and March 2020. Independent sample t-tests, chi-square tests, and logistic regression models were used to evaluate the correlation between clinicopathological data, systemic inflammation markers, and pCR. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for NLR, PLR, and LMR. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A predictive model for pCR was constructed using machine learning algorithms.

Results: Among the 209 breast cancer patients, 29 achieved pCR. During a median follow-up of 68 months, 74 patients experienced local or distant metastasis, and 56 patients died. Univariate logistic regression analysis showed that lymph node status, HER-2 status, NLR, PLR, and LMR were associated with pCR. ROC curve analysis revealed that the optimal cut-off values for NLR, PLR, and LMR were 1.525, 113.620, and 6.225, respectively. Multivariate logistic regression analysis indicated that lymph node status, NLR, and LMR were independent predictive factors for pCR. Survival analysis demonstrated that lymph node status, NLR, and LMR were associated with prognosis. Machine learning algorithm analysis identified the random forest (RF) model as the optimal predictive model for pCR.

Conclusion: This study demonstrated that lymph node status, NLR, and LMR had significant value in predicting pCR and prognosis in breast cancer patients. The RF model provides a simple and cost-effective tool for pCR prediction, offering strong support for clinical decision-making in breast cancer treatment and aiding in the optimization of individualized treatment strategies.