Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Journal: Nature communications
PMID: 32144248

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, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Authors

  • Xueyi Zheng
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Zhao Yao
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Yini Huang
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Yanyan Yu
    Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
  • Yun Wang
    Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China.
  • Yubo Liu
    Department of Cardiology, Xiangya Hospital, Central South University; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008.
  • Rushuang Mao
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Yang Xiao
  • Yuanyuan Wang
    Department of Biotechnology, College of Life Science and Technology, Jinan University Guangzhou, 510632, China.
  • Yixin Hu
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Jinhua Yu
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. jhyu@fudan.edu.cn.
  • Jianhua Zhou
    Department of Ultrasound, Sun Yat-sen University Cancer centre, State Key Laboratory of Oncology in South China, Collaborative Innovation centre for Cancer Medicine, Guangzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals