Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning.

Journal: Academic radiology
Published Date: Sep 10, 2024

Abstract

RATIONALE AND OBJECTIVES: Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiomic features from Automated Breast Volume Scanning (ABVS) images of different peritumoral region sizes to identify the optimal size for accurate preoperative Ki-67 prediction.

Authors

  • Bin Hu
    Department of Thoracic Surgery Beijing Chao-Yang Hospital Affiliated Capital Medical University Beijing China.
  • Yanjun Xu
    Department of Ultrasonography, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (Y.X.); Shanghai Institute of Ultrasound in Medicine, Shanghai, China (Y.X.).
  • Huiling Gong
    Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China (B.H., H.G., L.T.).
  • Lang Tang
    Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China (B.H., H.G., L.T.).
  • Hongchang Li
    Shenzhen Key Laboratory for Molecular Biology of Neural Development, Guangdong Key Laboratory of Nanomedicine, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.

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