A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Journal: International journal of surgery (London, England)
PMID: 38215256

Abstract

BACKGROUND: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients.

Authors

  • Jianguo Lai
    Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong.
  • Zijun Chen
    The Second Clinical School of Southern Medical University, Guangzhou.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Chao Zhu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Haoxuan Huang
    Department of Urology, Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China.
  • Ying Yi
    Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong.
  • Gengxi Cai
    Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, Guangdong.
  • Ning Liao
    Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong.

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