Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
Sun Yat-sen University · Sun Yat-sen University Cancer Center · +4 more institutions
Abstract
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843,…
Citation impact
- FWCI
- 38.64
- Percentile
- 100%
- References
- 60
Authors
13- XZXueyi ZhengCorresponding
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- ZYZhao Yao
Fudan University
- YHYini Huang
Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
- YYYanyan Yu
Shenzhen Institutes of Advanced Technology
- YWYun Wang
Sun Yat-sen University, Fudan University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
Topics & keywords
- Breast cancer
- Medicine
- Stage (stratigraphy)
- Axilla
- Receiver operating characteristic
- Oncology
- Axillary Lymph Node Dissection
- Radiology
- Reduced inequalities