A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients

Author:

Zeng De,Lin Hao-Yu,Zhang Yu-Ling,Wu Jun-Dong,Lin Kun,Xu Ya,Chen Chun-Fa

Abstract

AbstractExtensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1–2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0–2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0–2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829–0.921, P < 0.001) in the modeling group and 0.758 (95% confidence interval 0.702–0.807, P < 0.001) in the validation group. Decision curve analysis demonstrated that the model was clinically useful. The NBR model demonstrated adequate discriminative ability and clinical utility for predicting 0–2 ALN metastases.

Funder

Natural Science Foundation of Guangdong Province

Science and Technology Project of Shantou, China

Youth Scientific Research Fund of Cancer Hospital affiliated to Shantou University Medical College

2018 Special Fund for Science and Technology Innovation Strategy of Guangdong Province

2019 Science and Technology Special Fund of Guangdong Province

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3