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作者

Bi, Zhao Song, Xianrang Chen, Peng Xie, Li Jiang, Wei Song, Xingguo Cong, Binbin Wang, Yongsheng

作者单位

Breast Surgical Ward I, Shandong Cancer Hospital and Institute, Shandong Province, Jinan, 250000, China Laboratory, Shandong Cancer Hospital and Institute, Shandong Province, Jinan, 250000, China School of Medicine and Life Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Province, Jinan, 250000, China Obstetrics Second Ward, Peoples Hospital of Juxian, Shandong Province, Rizhao, 276599, China

刊名

China Oncology

年份

2021

卷号

Vol.31 No.1

页码

63

ISSN

1007-3639

摘要

Background and purpose: Neutrophil to lymphocyte ratio is a simple, objective and inexpensive laboratory indicator, and its predictive value has been verified in different types of cancer. The purpose of this study was to integrate pretreatment indicators including clinical factors with NLR to predict axillary pathological complete response after neoadjuvant therapy . Methods: From April 2016 to April 2020, 416 breast cancer patients with clinical nodal positive disease undergoing operation af...更多

Background and purpose: Neutrophil to lymphocyte ratio is a simple, objective and inexpensive laboratory indicator, and its predictive value has been verified in different types of cancer. The purpose of this study was to integrate pretreatment indicators including clinical factors with NLR to predict axillary pathological complete response after neoadjuvant therapy . Methods: From April 2016 to April 2020, 416 breast cancer patients with clinical nodal positive disease undergoing operation after NAT were included. The pretreatment clinicopathological factors and laboratory indexes were collected. The optimal cut-off values of age and laboratory indexes were determined by Youden index using receiver operating characteristic curve analyses. The logistic regression analysis was applied to examine predictive factors of apCR. Then, a logistic model was developed according to multivariate analysis results, and it was analyzed using ROC curve and area under curve value. Results: Among 416 patients, 37.3% of them achieved apCR. The multivariate analysis showed that age , pathological grade , molecular subtype and NLR were indicated as independent predictors of apCR. Based on these factors, we built the logistic model to predict apCR: Logit=0.613 xpathological grading+1.027xmolecular subtype-0.638xage-1.196xNLR-0.244 . The AUC value of the logistic model was 0.702. Conclusion: Except for traditional clinical factors, the NLR level could also be identified as predictive factor of apCR after NAT. Integrating traditional clinical factors with NLR level could help to predict apCR and guide individualized treatment options.收起

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