Machine Learning Models Based on Stretched-Exponential Diffusion Weighted Imaging to Predict TROP2 Expression in Nude Mouse Breast Cancer Models.

Journal: Discovery medicine
PMID: 40116097

Abstract

BACKGROUND: Trophoblast cell surface antigen 2 (TROP2) is a promising target for various cancers, including breast cancer. The development of noninvasive techniques for assessing TROP2 expression in tumors holds considerable importance. This study aims to explore the efficacy of machine learning models based on multi-b-value diffusion-weighted imaging (DWI) using the stretched-exponential model (SEM) for predicting TROP2 expression in breast cancer in nude mouse models.

Authors

  • Yi Deng
    Faculty of Life and Biotechnology, Institute of Kunming University of Science and Technology, Kunming, China.
  • Chao-Gang Han
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.
  • Zi-Qin Deng
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.
  • Shou-Yi Yang
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.
  • Zhuo-Han Wu
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.
  • Jia-Li Liu
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.
  • Jia-Ming Ma
    Department of Radiology, Shaoguan Maternal and Child Health Hospital, 512000 Shaoguan, Guangdong, China.

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