Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.

Journal: Abdominal radiology (New York)
PMID: 39276192

Abstract

OBJECTIVE: To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI.

Authors

  • Xianhong Wang
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Qiu Bi
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Cheng Deng
  • Yaoxin Wang
    Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, the Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
  • Yunbo Miao
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Ruize Kong
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Chenrong Li
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Xiulan Liu
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Xiarong Gong
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
  • Ya Zhang
    Department of Plant Protection, College of Plant Protection, Hunan Agricultural University, Changsha, China. Electronic address: zhangya230@126.com.
  • Guoli Bi
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China. guolibi76@aliyun.com.

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