Feasibility study of predicting axillary lymph node metastasis of breast cancer using radiomics analysis based on dynamic contrast-enhanced MRI
10.3760/cma.j.cn112149-20210810-00460
- VernacularTitle:基于乳腺癌动态增强MRI图像的影像组学特征预测腋窝淋巴结转移的可行性研究
- Author:
Yuan JIANG
1
;
Mingming MA
;
Yuanjia CHENG
;
Yingpu CUI
;
Changxin LI
;
Yaofeng ZHANG
;
Xiaodong ZHANG
;
Xiaoying WANG
;
Naishan QIN
Author Information
1. 北京大学第一医院医学影像科, 北京 100034
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Axillary lymph node metastasis;
Radiomics
- From:
Chinese Journal of Radiology
2022;56(6):631-635
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of predicting axillary lymph node metastasis of breast cancer using radiomics analysis based on dynamic contrast-enhanced (DCE) MRI.Methods:The retrospective study enrolled 163 patients (163 lesions) with breast cancer diagnosed by core needle biopsy from January 2013 to December 2013 in Peking University First Hospital. The status of axillary lymph nodes in all patients was pathologically confirmed, and they had complete preoperative breast MRI images. Among the 163 patients, 94 patients were confirmed with axillary lymph node metastasis, and 69 patients without axillary lymph node metastasis. They were randomly divided into the training dataset ( n=115) and testing dataset ( n=48) in a 7∶3 ratio. The radiomics analysis was performed in the training dataset, including image preprocessing and labeling, radiomics feature extraction, radiomics model establishment and model predictive performance inspection. Model performance was tested in the testing dataset. Receiver operating characteristic curve and area under curve (AUC) was used to analyze the model prediction performance. Results:Of the 1 075 features extracted from the training dataset, principal component analyses (PCA) features 8, 41 and 67 were selected by random forest classifier. The radiomics model including 3 PCA features reached an AUC of 0.956 (95%CI 0.907-0.988), with sensitivity of 91.2%, specificity of 100% and accuracy of 94.8%. In the testing dataset, the radiomics model including 3 PCA features reached an AUC of 0.767 (95%CI 0.652-0.890), with sensitivity of 80.8%, specificity of 72.7% and accuracy of 77.1%.Conclusion:It is feasible to predict axillary lymph node metastasis using radiomics features based on DCE-MRI of breast cancer.
