A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists.

Journal: Academic radiology
PMID: 38182443

Abstract

RATIONALE AND OBJECTIVES: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images.

Authors

  • Xiaoling Liu
    Department of Endocrinology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
  • Xiachuan Qin
    Department of Ultrasound, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, China.
  • Qi Luo
    B-DAT & CICAEET, School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.
  • Jing Qiao
    Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China (J.Q.).
  • Weihan Xiao
    Department of Ultrasound, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, China.
  • Qiwei Zhu
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.).
  • Jian Liu
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Chaoxue Zhang
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

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