Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients
Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients
复制标题
基于袋装树的早期乳腺癌患者前哨淋巴结转移预测模型的建立和验证。
DOI：
10.3389/fonc.2019.00282
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发表时间：
2019-04-16
影响因子：
4.7
通讯作者：
Liu, Caigang
中科院分区：
文献类型：
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作者：
Liu, Chao;Zhao, Zeyin;Liu, Caigang
目的：淋巴结转移是一个多因素事件。一些学者开发了列线图模型来预测术前前哨淋巴结（SLN）转移。根据乳腺癌患者的临床和病理特点，我们采用新方法建立了更全面的模型，加入了一些国际上从未分析过的新因素，探讨了其临床应用前景。材料与方法：对2011年1月至2014年12月期间接受SLN检查的633例乳腺癌患者的临床病理资料进行回顾性分析。由于数据不平衡，我们使用smote算法对数据进行过采样，以增加数据的平衡量。我们的研究首次包括肿瘤的形状和乳腺内容。采用向量结合象限法分析肿瘤的位置，同时采用单纯使用象限或向量进行比较的方法。我们还比较了通过逻辑回归和 Bagged-Tree 算法构建模型的预测能力。 Bagged-Tree 算法用于对样本进行分类。采用SMOTE-Bagged Tree算法和5折交叉验证建立预测模型。通过混淆矩阵和受试者工作特征（ROC）曲线下面积（AUC）评估该模型在早期乳腺癌患者中的临床应用价值。结果：我们的预测模型包括12个变量：年龄、体重指数（BMI）、象限、时钟方向、肿瘤距乳头的距离、肿瘤钼靶的形态、腺体内容物、肿瘤大小、ER、PR、HER2和Ki-67。最终，我们的模型获得了0.801的AUC值和70.3%的准确率。我们使用逻辑回归来建立模型，在建模组和验证组中，曲线下面积（AUC）分别为0.660和0.580。我们使用向量结合象限的方法来分析肿瘤的原始位置，比单纯使用向量或象限更精确（AUC 0.801 vs. 0.791 vs. 0.701，准确率70.3 vs. 70.3 vs. 63.6%）。结论：我们的模型更加可靠和稳定，可以帮助医生在术前预测乳腺癌患者的SLN转移。
Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67. Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.