Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy.

Journal: Academic radiology
PMID: 39183131

Abstract

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Additionally, we employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm.

Authors

  • Yue-Xia Liu
    Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Qing-Hua Liu
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Quan-Hui Hu
    Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Jia-Yao Shi
    Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Gui-Lian Liu
    Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Han Liu
    Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things, Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
  • Sheng-Chun Shu
    Department of Ultrasound, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. Electronic address: shengchunshu@163.com.

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