Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

Journal: Frontiers in oncology
Published Date: Jan 14, 2025

Abstract

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

Authors

  • Pu Zhou
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
  • Hongyan Qian
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
  • Pengfei Zhu
    Department of Ultrasound, Affiliated Tumor Hospital of Nantong University, Jiangsu, Nantong, China.
  • Jiangyuan Ben
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
  • Guifang Chen
    Department of Ultrasound, Affiliated Tumor Hospital of Nantong University, Jiangsu, Nantong, China.
  • Qiuyi Chen
    Department of Ultrasound, Affiliated Tumor Hospital of Nantong University, Jiangsu, Nantong, China.
  • Lingli Chen
    Department of Surgery, Affiliated Tumor Hospital of Nantong University, Nantong, China.
  • Jia Chen
    Department of Oncology Internal Medicine, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, China.
  • Ying He
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.

Keywords

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