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学科主题临床医学
A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients
Xie, Fei; Yang, Houpu; Wang, Shu; Zhou, Bo; Tong, Fuzhong; Yang, Deqi; Zhang, Jiaqing
关键词breast cancer axillary metastases predictive model logistic regression lymph node staging
刊名SENSORS
2012-07-01
DOI10.3390/s120709936
12期:7页:9936-9950
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
研究领域[WOS]Chemistry ; Electrochemistry ; Instruments & Instrumentation
关键词[WOS]POSITIVE SENTINEL NODE ; CANCER PATIENTS ; SUPPRESSOR GENE ; INVOLVEMENT ; DISSECTION ; BIOPSY ; EXPRESSION ; SURGERY ; NOMOGRAM ; NM23-H1
英文摘要

Nodal staging in breast cancer is a key predictor of prognosis. This paper presents the results of potential clinicopathological predictors of axillary lymph node involvement and develops an efficient prediction model to assist in predicting axillary lymph node metastases. Seventy patients with primary early breast cancer who underwent axillary dissection were evaluated. Univariate and multivariate logistic regression were performed to evaluate the association between clinicopathological factors and lymph node metastatic status. A logistic regression predictive model was built from 50 randomly selected patients; the model was also applied to the remaining 20 patients to assess its validity. Univariate analysis showed a significant relationship between lymph node involvement and absence of nm-23 (p = 0.010) and Kiss-1 (p = 0.001) expression. Absence of Kiss-1 remained significantly associated with positive axillary node status in the multivariate analysis (p = 0.018). Seven clinicopathological factors were involved in the multivariate logistic regression model: menopausal status, tumor size, ER, PR, HER2, nm-23 and Kiss-1. The model was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.702 when applied to the validation group. Moreover, there is a need discover more specific candidate proteins and molecular biology tools to select more variables which should improve predictive accuracy.

语种英语
WOS记录号WOS:000306796500081
引用统计
被引频次:17[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
版本出版稿
条目标识符http://ir.bjmu.edu.cn/handle/400002259/53674
专题北京大学第二临床医学院_乳腺外科
作者单位Peking Univ, Peoples Hosp, Breast Dis Ctr, Beijing 100044, Peoples R China
推荐引用方式
GB/T 7714
Xie, Fei,Yang, Houpu,Wang, Shu,et al. A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients[J]. SENSORS,2012,12(7):9936-9950.
APA Xie, Fei.,Yang, Houpu.,Wang, Shu.,Zhou, Bo.,Tong, Fuzhong.,...&Zhang, Jiaqing.(2012).A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients.SENSORS,12(7),9936-9950.
MLA Xie, Fei,et al."A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients".SENSORS 12.7(2012):9936-9950.
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