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早期初治淋巴结阳性乳腺癌新辅助化疗后淋巴结病理完全缓解预测模型的建立与验证

Establishment and Verification of a Predictive Model for Node Pathological Complete Response After Neoadjuvant Chemotherapy for Initial Node Positive Early Breast Cancer.

作者信息

Zhu Jiujun, Jiao Dechuang, Yan Min, Chen Xiuchun, Wang Chengzheng, Lu Zhenduo, Li Lianfang, Sun Xianfu, Qin Li, Guo Xuhui, Zhang Chongjian, Qiao Jianghua, Li Jianbin, Fan Zhimin, Wang Haibo, Zhang Jianguo, Yin Yongmei, Fu Peifen, Geng Cuizhi, Jin Feng, Jiang Zefei, Cui Shude, Liu Zhenzhen

机构信息

Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.

Department of Breast Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

出版信息

Front Oncol. 2021 Apr 29;11:675070. doi: 10.3389/fonc.2021.675070. eCollection 2021.

Abstract

OBJECTIVE

Axillary node status after neoadjuvant chemotherapy (NCT) in early breast cancer patients influences the axillary surgical staging procedure. This study was conducted for the identification of the likelihood of patients being node pathological complete response (pCR) post NCT. We aimed to recognize patients most likely to benefit from sentinel lymph node biopsy (SLNB) following NCT and to reduce the risk of missed detection of positive lymph nodes through the construction and validation of a clinical preoperative scoring prediction model.

METHODS

The existing data (from March 2010 to December 2018) of the Chinese Society of Clinical Oncology Breast Cancer Database (CSCO-BC) was used to evaluate the independent related factors of node pCR after NCT by Binary Logistic Regression analysis. A predictive model was established according to the score of considerable factors to identify ypN0. Model performance was confirmed in a cohort of NCT patients treated between January 2019 and December 2019 in Henan Cancer Hospital, and model discrimination was evaluated via assessing the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Multivariate regression analysis showed that the node stage before chemotherapy, the expression level of Ki-67, biologic subtype, and breast pCR were all independent related factors of ypN0 after chemotherapy. According to the transformation and summation of odds ratio (OR) values of each variable, the scoring system model was constructed with a total score of 1-5. The AUC for the ROC curves was 0.715 and 0.770 for the training and the validation set accordingly.

CONCLUSIONS

A model was established and verified for predicting ypN0 after chemotherapy in newly diagnosed cN+ patients and the model had good accuracy and efficacy. The underlined effective model can suggest axillary surgical planning, and reduce the risk of missing positive lymph nodes by SLNB after NCT. It has great value for identifying initial cN+ patients who are more appropriate for SLNB post-chemotherapy.

摘要

目的

早期乳腺癌患者新辅助化疗(NCT)后的腋窝淋巴结状态会影响腋窝手术分期程序。本研究旨在确定患者在NCT后达到淋巴结病理完全缓解(pCR)的可能性。我们的目标是识别出最有可能从NCT后的前哨淋巴结活检(SLNB)中获益的患者,并通过构建和验证临床术前评分预测模型来降低漏诊阳性淋巴结的风险。

方法

使用中国临床肿瘤学会乳腺癌数据库(CSCO-BC)的现有数据(2010年3月至2018年12月),通过二元逻辑回归分析评估NCT后淋巴结pCR的独立相关因素。根据相关因素的评分建立预测模型以识别ypN0。在2019年1月至2019年12月期间在河南省肿瘤医院接受治疗的NCT患者队列中对模型性能进行验证,并通过评估受试者操作特征(ROC)曲线下面积(AUC)来评估模型的辨别力。

结果

多变量回归分析表明,化疗前的淋巴结分期、Ki-67表达水平、生物学亚型和乳腺pCR均为化疗后ypN0的独立相关因素。根据各变量比值比(OR)值的转换和求和,构建了总分1至5分的评分系统模型。训练集和验证集的ROC曲线AUC分别为0.715和0.770。

结论

建立并验证了一个用于预测新诊断的cN+患者化疗后ypN0的模型,该模型具有良好的准确性和有效性。该有效模型可指导腋窝手术规划,并降低NCT后SLNB漏诊阳性淋巴结的风险。对于识别更适合化疗后SLNB的初始cN+患者具有重要价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/395a/8117332/2b14c6ca6361/fonc-11-675070-g001.jpg

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