Open AccessPreoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI
Author(s) -
Xiaoyu Cui,
Nian Wang,
Yue Zhao,
Shuo Chen,
Songbai Li,
Mingyu Xu,
Ruimei Chai
Publication year - 2019
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-019-38502-0
Subject(s) - breast cancer , medicine , lymph node , lymph node metastasis , magnetic resonance imaging , support vector machine , classifier (uml) , axillary lymph nodes , metastasis , lymph , radiology , segmentation , pattern recognition (psychology) , artificial intelligence , computer science , cancer , pathology
The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation. The results showed that the effect of the SVM classifier in predicting breast axillary lymph node metastasis was significantly higher than that of the KNN classifier and LDA classifier. The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. Each feature was analyzed separately and the results showed that the effect of feature combination was obviously better than that of any individual feature on its own.