您当前的位置：
首页 >
文章列表页 >
Risk assessment of sentinel lymph node metastasis in breast cancer using a radiomics model based on B-mode ultrasound and color doppler ultrasound
更新时间：2025-12-15

    • Risk assessment of sentinel lymph node metastasis in breast cancer using a radiomics model based on B-mode ultrasound and color doppler ultrasound

    • “In the field of prediction of sentinel lymph node metastasis of breast cancer, experts from Shanghai Pudong New Area People's Hospital established a joint model of gray-scale ultrasound imaging omics characteristics and color Doppler ultrasound blood flow characteristics, which significantly improved the prediction accuracy and provided important reference for accurate diagnosis and treatment of breast cancer.”
    • Oncoradiology   Vol. 34, Issue 4, Pages: 363-370(2025)

    • DOI：10.19732/j.cnki.2096-6210.2025.04.007    

      CLC：

    • Received：19 February 2025，

      Published Online：04 September 2025，

      Published：28 August 2025

    移动端阅览

  • Yicheng ZHU, Yuan ZHANG, Zheqin YANG, et al. Risk assessment of sentinel lymph node metastasis in breast cancer using a radiomics model based on B-mode ultrasound and color doppler ultrasound[J]. Oncoradiology, 2025, 34(4): 363-370. DOI： 10.19732/j.cnki.2096-6210.2025.04.007.

  •  
  •  

0

Views

141

下载量

0

CNKI被引量

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Value of deep learning ultrasound radiomics in predicting axillary lymph node metastasis of breast cancer
Multimodal ultrasound features combined with serum CEA and CK19 to predict axillary lymph node metastasis of breast cancer
Deep learning model based on two-dimensional ultrasound images for preoperative prediction of breast cancer lymphovascular invasion: a human-machine comparison study
Prediction of sentinel lymph node metastasis in breast cancer using multiparametric MRI radiomics and machine learning models
Research progress of ultrasound in molecular typing prediction of breast cancer

Related Author

Jiaojiao HU
Xiaohong FU
Yan SHEN
Xiaoqing YU
Qingqing CHEN
Su HU
Yuqing HE
Zizheng WU

Related Institution

Department of Radiology, The First Affiliated Hospital of Soochow University
Department of Ultrasound, Gongli Hospital, Shanghai Pudong New Area
Institute of Medical Imaging, Soochow University
Department of Ultrasound, The First Hospital of Qinhuangdao
Qinhuangdao Health School, Qin Huangdao
0