Intratumoral and peritumoral radiomics based on 2D ultrasound imaging in breast cancer was used to determine the optimal peritumoral range for predicting KI-67 expression.

Journal: Journal of ultrasound
Published Date:

Abstract

OBJECTIVES: Currently, radiomics focuses on intratumoral regions and fixed peritumoral regions, and lacks an optimal peritumoral region taken to predict KI-67 expression. The aim of this study was to develop a machine learning model to analyze ultrasound radiomics features with different regions of peri-tumor fetch values to determine the optimal peri-tumor region for predicting KI-67 expression.

Authors

  • Wangxing Huang
    Graduate School of Qinghai University, Xining, China.
  • Songming Zheng
    School of Computer Technology and Application, Qinghai University, Xining, China.
  • Xiaoyan Zhang
    Institute of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi, China.
  • Lina Qi
    Interventional Ultrasound Department, Qinghai Provincial People's Hospital, Xining, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Qinghua Zhang
    Department of Obstetrics and Gynecology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhen Zhen
    School of Forestry, Northeast Forestry University, Harbin 150040, China.
  • Xiuwei Yang
    Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250100, China.
  • Changqin Kong
    Interventional Ultrasound Department, Qinghai Provincial People's Hospital, Xining, China.
  • Dong Li
    Department of Cardiovascular Medicine, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China.
  • Guoyong Hua
    Interventional Ultrasound Department, Qinghai Provincial People's Hospital, Xining, China. 58428445@qq.com.

Keywords

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