Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

Journal: Academic radiology
Published Date: Jun 1, 2023

Abstract

RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

Authors

  • Jia-Xin Huang
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.).
  • Jun Shi
    School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: junshi@staff.shu.edu.cn.
  • Sai-Sai Ding
    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China (J.S., S.-S.D., H.-L.Z.).
  • Hui-Li Zhang
    School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China (J.S., S.-S.D., H.-L.Z.).
  • Xue-Yan Wang
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.).
  • Shi-Yang Lin
    Department of Medical Ultrasound, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510000, China (S.-Y.L.).
  • Yan-Fen Xu
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.).
  • Ming-Jie Wei
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.).
  • Long-Zhong Liu
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.).
  • Xiao-Qing Pei
    Department of Medical Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China (J.-X.H., X.-Y.W., Y.-F.X., M.-J.W., L.-Z.L., X.-Q.P.). Electronic address: peixq@sysucc.org.cn.

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