Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.

Journal: Abdominal radiology (New York)
Published Date: Jan 2, 2025

Abstract

OBJECTIVE: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI.

Authors

  • Yuan Chen
    Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032.
  • Xiaohong Ruan
    Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China.
  • Ximiao Wang
    School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
  • Peijun Li
    Department of Radiology, 71537Jiangmen Central Hospital, Jiangmen, Guangdong Province, PR China.
  • Yehang Chen
    Biomedical and Artificial Intelligence Laboratory, Guilin University of Aerospace Technology, Guilin, China.
  • Bao Feng
    The Department of Radiology, Jiangmen Central Hospital/Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
  • Xianyan Wen
    Jiangmen Central Hospital, Jiangmen, China.
  • Junqi Sun
    Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York.
  • Changye Zheng
    Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
  • Yujian Zou
    Department of Radiology, The People's Hospital of Dongguan, Dongguan, Guangdong, China.
  • Bo Liang
    Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
  • Mingwei Li
    State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China.
  • Wansheng Long
    Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Guangdong Medical University, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China.
  • Yuan Shen
    Department of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, Jiangsu, China.

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