Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.

Journal: Frontiers in physiology
Published Date: Dec 20, 2024

Abstract

OBJECTIVES: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.

Authors

  • Bing Wen
    Department of Radiology, Yiyang Central Hospital, Yiyang, China.
  • Chengwei Li
    Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, China.
  • Qiuyi Cai
    Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, China.
  • Dan Shen
    Department of Radiology, Yiyang Central Hospital, Yiyang, China.
  • Xinyi Bu
    Department of Radiology, Yiyang Central Hospital, Yiyang, China.
  • Fuqiang Zhou
    Department of Radiology, Yiyang Central Hospital, Yiyang, China.

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