Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Journal: Breast cancer research and treatment
Published Date: May 16, 2025

Abstract

PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application of machine learning (ML) based peritumoral ultrasound radiomics signature (PURS), compared with intratumoral radiomics (IURS) and clinicopathologic factors, for early prediction of pCR.

Authors

  • Jiejie Yao
    Department of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Xiaohong Jia
    Ruijin Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China.
  • Ying Zhu
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Xiaosong Chen
    Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China. chenxiaosong0156@hotmail.com.
  • Weiwei Zhan
    Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
  • JianQiao Zhou
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, China. zhousu30@126.com.

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