A study of neutrophil to lymphocyte ratio for the prediction of axillary pathological complete response after neoadjuvant therapy in breast cancer

毕　钊; 宋现让; 陈　鹏; 谢　丽; 蒋　伟; 宋兴国; 丛斌斌; 王永胜

doi:10.19401/j.cnki.1007-3639.2021.01.008

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A study of neutrophil to lymphocyte ratio for the prediction of axillary pathological complete response after neoadjuvant therapy in breast cancer

更新时间：2026-01-27

- A study of neutrophil to lymphocyte ratio for the prediction of axillary pathological complete response after neoadjuvant therapy in breast cancer
- China Oncology Vol. 31, Issue 1, Pages: 63-68(2021)
- 作者机构：
  
  1. 山东第一医科大学（山东省医学科学院）医学与生命科学学院,山东,济南,250000
  2. 山东省肿瘤防治研究院（山东省肿瘤医院）乳腺外科一病区,山东,济南,250000
  3. 山东省肿瘤防治研究院（山东省肿瘤医院）检验科,山东,济南,250000
  4. 山东省莒县人民医院产科二病区,山东,日照,276599
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2021.01.008
  CLC： R737.9
- Published Online：22 February 2021，
  
  Published：22 February 2021
- 稿件说明：
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毕　钊, 宋现让, 陈　鹏, 谢　丽, 蒋　伟, 宋兴国, 丛斌斌, 王永胜 . 中性粒细胞与淋巴细胞比值预测乳腺癌新辅助治疗后腋窝淋巴结病理学完全缓解的研究[J]. 中国癌症杂志, 2021, 31(1): 63-68.

毕　钊, 宋现让, 陈　鹏, et al. A study of neutrophil to lymphocyte ratio for the prediction of axillary pathological complete response after neoadjuvant therapy in breast cancer[J]. China Oncology, 2021, 31(1): 63-68.
毕　钊, 宋现让, 陈　鹏, 谢　丽, 蒋　伟, 宋兴国, 丛斌斌, 王永胜 . 中性粒细胞与淋巴细胞比值预测乳腺癌新辅助治疗后腋窝淋巴结病理学完全缓解的研究[J]. 中国癌症杂志, 2021, 31(1): 63-68. DOI： 10.19401/j.cnki.1007-3639.2021.01.008.

毕　钊, 宋现让, 陈　鹏, et al. A study of neutrophil to lymphocyte ratio for the prediction of axillary pathological complete response after neoadjuvant therapy in breast cancer[J]. China Oncology, 2021, 31(1): 63-68. DOI： 10.19401/j.cnki.1007-3639.2021.01.008.

摘要

背景与目的：中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio，NLR）作为简单、客观且廉价的实验室指标，其疗效预测价值已在其他类型的癌种中得到验证。整合新辅助治疗（neoadjuvant therapy，NAT）前临床病理学特征及NLR来预测NAT后腋窝淋巴结病理学完全缓解（axillary pathological complete response，apCR）率，以期指导临床决策。方法：入组2016年4月—2020年4月山东省肿瘤医院乳腺外科连续收治的416例接受NAT的临床淋巴结阳性患者。入组患者接受完整疗程蒽环类药物联合紫杉类药物治疗，回顾性分析患者接受NAT前的临床病理学特征及实验室指标。临床病理学特征作为分类变量，年龄及实验室指标作为连续变量，其cut-off值通过受试者工作特征（receiver operating characteristic，ROC）曲线的约登指数来确定。多因素logistic回归分析确定NAT后apCR的影响因素，同时通过logistic回归分析结果进一步构建NAT后apCR预测模型，采用ROC曲线及曲线下面积（area under curve，AUC）对模型进行评价。结果：入组的416例患者中，37.3%（155/416）的患者达到apCR。单因素分析显示，apCR与年龄、穿刺病理学分级、分子分型、血糖、血小板、NLR显著相关（P均＜0.05）。多因素分析显示，年龄（OR=0.528，95% CI：0.343~0.814）、穿刺病理学分级（OR=1.846，95% CI：1.187~2.872）、分子分型（OR=2.791，95% CI：1.780~4.377）和NLR（OR=0.302，95% CI：0.105~0.867）是NAT后apCR的独立影响因素（P均＜0.05）。基于这些影响因素，我们构建了NAT后apCR的logistic预测模型：logit(P)=0.613×病理学分级+1.027×分子分型-0.638×年龄-1.196×NLR-0.244（模型检验χ

=54.478，P＜0.001），该模型的AUC为0.702。结论：除了传统的临床病理学特征，作为简单、廉价、客观的实验室指标，NLR水平也可以作为NAT后apCR的预测指标。整合NAT前临床病理学特征及NLR水平可

以帮助预测NAT后apCR，进而基于NAT疗效选择个体化腋窝降阶梯手术处理。

Abstract

Background and purpose: Neutrophil to lymphocyte ratio (NLR) 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 (apCR) after neoadjuvant therapy (NAT). 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 (ROC) 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 (AUC) value. Results: Among 416 patients

37.3% (155) of them achieved apCR. The multivariate analysis showed that age (OR=0.528

95% CI: 0.343-0.814)

pathological grade (OR=1.846

95% CI: 1.187-2.872)

molecular subtype (OR=2.791

95% CI: 1.780-4.377) and NLR (OR=0.302

95% CI: 0.105-0.867) were indicated as independent predictors of apCR. Based on these factors

we built the logistic model to predict apCR: logit(P)=0.613×pathological grading+1.027×molecular subtype-0.638×age-1.196×NLR-0.244 (model checking χ

=54.478

P＜ 0.001). 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.

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