Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

作者：Chao Liu, Chao Liu, Zeyin Zhao, Xi Gu, Lisha Sun, Guanglei Chen, Hao Zhang, Yanlin Jiang, Yixiao Zhang, Xiaoyu Cui, Caigang Liu, Caigang Liu · 发表于：Frontiers in Oncology · 年份：2019 · DOI：10.3389/fonc.2019.00282 · 被引用次数：27 · 研究领域：Breast Cancer Treatment Studies、AI in cancer detection、Radiomics and Machine Learning in Medical Imaging

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. Material 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 include...