Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Radiomics Features of DCE-MRI

Overview

Journal Sci Rep

Specialty Science

Date 2019 Feb 21

PMID 30783148

Citations 37

Authors

Xiaoyu Cui

Nian Wang

Yue Zhao

Shuo Chen

Songbai Li

Mingjie Xu

Ruimei Chai

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis.

Dong F, Li J, Wang J, Yang X PLoS One. 2024; 19(12):e0314653.

PMID: 39625963 PMC: 11614294. DOI: 10.1371/journal.pone.0314653.

MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer.

Zhang C, Zhong M, Liang Z, Zhou J, Wang K, Bu J BMC Med Imaging. 2024; 24(1):322.

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Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.

Wang X, Nie L, Zhu Q, Zuo Z, Liu G, Sun Q BMC Cancer. 2024; 24(1):910.

PMID: 39075447 PMC: 11285453. DOI: 10.1186/s12885-024-12619-6.

Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer.

Yang X, Lu Z, Tan X, Shao L, Shi J, Dou W Quant Imaging Med Surg. 2024; 14(6):3789-3802.

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Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

Wang Q, Lin Y, Ding C, Guan W, Zhang X, Jia J Eur Radiol. 2024; 34(9):6121-6131.

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