Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study

被引:7
作者
Zhang, Jiwen [1 ]
Zhang, Zhongsheng [2 ]
Mao, Ning [2 ]
Zhang, Haicheng [2 ]
Gao, Jing [3 ]
Wang, Bin [4 ]
Ren, Jianlin [4 ]
Liu, Xin [4 ]
Zhang, Binyue [1 ]
Dou, Tingyao [1 ]
Li, Wenjuan [2 ]
Wang, Yanhong [5 ]
Jia, Hongyan [4 ]
机构
[1] Shanxi Med Univ, Dept Clin Med 1, Taiyuan, Peoples R China
[2] Qingdao Univ, Yantai Yuhuangding Hosp, Dept Radiol, Affiliated Hosp, Yantai, Peoples R China
[3] Binzhou Med Univ, Sch Med Imaging, Yantai, Peoples R China
[4] Shanxi Med Univ, Dept Breast Surg, Hosp 1, Taiyuan, Peoples R China
[5] Shanxi Med Univ, Dept Microbiol & Immunol, Taiyuan, Peoples R China
关键词
Breast cancer; Lymph node; Radiomics; Nomogram; Magnetic resonance imaging; MOLECULAR SUBTYPES; IMAGES; HETEROGENEITY;
D O I
10.3233/XST-221336
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
OBJECTIVES: This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwentDCE-MRIexamination before surgery in two hospitals. All patients had a definedALNstatus based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets. RESULTS: ALNMrates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone. CONCLUSIONS: The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner.
引用
收藏
页码:247 / 263
页数:17
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