AUTHOR=Jiang Chun , Zhu Tong , Zong Yunfei , Liu Ruibin , Du Jiang , Dai Junze , Song Yuxuan , Zhang Dingye , Wang Xin , Shi Zhaohu , Jiang Yinping , Bu Jiawen , Ding Baifang , Zhu Xudong 

TITLE=Defining the optimal Ki67 cutoff values for survival prediction in neoadjuvant chemotherapy-treated patients with breast cancer

JOURNAL=Frontiers in Surgery

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1697963

DOI=10.3389/fsurg.2025.1697963

ISSN=2296-875X

ABSTRACT=BackgroundThe rising incidence of breast cancer underscores the need for precise prognostic assessment following neoadjuvant chemotherapy (NAC). Ki67 is widely utilized for prognostic evaluation. However, its clinical applicability remains debated, particularly regarding the optimal cutoff threshold. This study aims to establish the optimal Ki67 cutoff value and evaluate its prognostic significance in predicting survival outcomes in patients with breast cancer undergoing NAC.MethodsA retrospective analysis was performed on 255 patients with breast cancer who received NAC between 2011 and 2024. The optimal Ki67 cutoff value was determined using maximally selected rank statistics. Kaplan–Meier survival analysis was used to evaluate the impact of Ki67 on disease-free survival (DFS) and overall survival (OS). Prognostic variables were selected via Cox regression analysis combined with LASSO dimensionality reduction. Based on these findings, nomogram models incorporating Ki67 and other clinical parameters were constructed to predict 1-year, 3-year, and 5-year DFS and OS, and the models were subsequently evaluated.ResultsAs a continuous variable, Ki67 presented an increasing and non-linear association with the risk of DFS. Using 20% as the threshold, survival analysis indicated that patients with a high Ki67 proliferation index (Ki67 > 20%) had significantly shortened DFS and OS compared to those with low Ki67 proliferation index. Cox regression analysis also confirmed that Ki67 was a common independent prognostic predictor for both DFS and OS. The nomogram model integrating Ki67, T stage, N stage, and other clinical parameters exhibited strong predictive performance, with the area under the curve (AUC) exceeding 0.900 at all-time points. Calibration plot further validated the model's accuracy, with a C-index of 0.894 for DFS and 0.788 for OS.ConclusionsA Ki67 cutoff of 20% serves as a reliable predictor of DFS and OS in patients with breast cancer receiving NAC. The developed nomogram models, incorporating Ki67 and other clinical parameters, provide an accurate and clinically valuable tool for individualized prognostic assessment.