Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

Journal: Academic radiology
Published Date: Jan 5, 2024

Abstract

RATIONALE AND OBJECTIVES: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions.

Authors

  • Fang Li
    Department of General Surgery, Chongqing General Hospital, Chongqing, China.
  • Tong-Wei Zhu
    Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Linhai, Zhejiang, China (T.Z.).
  • Miao Lin
    Second Department of General Surgery, The People's Hospital of Yuhuan, Yuhuan, Zhejiang, China (M.L.).
  • Xiao-Ting Zhang
    Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.).
  • Ya-Li Zhang
    Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.).
  • Ai-Li Zhou
    Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.).
  • De-Yi Huang
    Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.). Electronic address: Huangdy18958676710@163.com.

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