A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients.
A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients.
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DOI：
10.3390/s120709936
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Xie F;Yang H;Wang S;Zhou B;Tong F;Yang D;Zhang J
乳腺癌的淋巴结分期是预后的关键预测因素。本文介绍了腋窝淋巴结受累的潜在临床病理学预测因子的结果，并开发了一种有效的预测模型来协助预测腋窝淋巴结转移。对 70 名接受腋窝淋巴结清扫术的原发性早期乳腺癌患者进行了评估。采用单变量和多变量逻辑回归来评估临床病理因素与淋巴结转移状态之间的关联。根据 50 名随机选择的患者建立逻辑回归预测模型；该模型还应用于其余 20 名患者以评估其有效性。单变量分析显示淋巴结受累与 nm-23 (p = 0.010) 和 Kiss-1 (p = 0.001) 表达缺失之间存在显着关系。在多变量分析中，Kiss-1 的缺失仍然与腋窝淋巴结阳性状态显着相关 (p = 0.018)。多变量逻辑回归模型涉及七个临床病理因素：绝经状态、肿瘤大小、ER、PR、HER2、nm-23 和 Kiss-1。该模型准确且具有辨别力，应用于验证组时，受试者工作特征曲线下面积为 0.702。此外，需要发现更具体的候选蛋白质和分子生物学工具来选择更多的变量，从而提高预测的准确性。
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.
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影响因子：
1.2
作者：
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通讯作者：
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