Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer.

Journal: Academic radiology
Published Date: Aug 27, 2023

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

Authors

  • Mingxiang Wei
    Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
  • Guannan Feng
    Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
  • Xinyi Wang
    School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.
  • Jianye Jia
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Yao Dai
    Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Cai Qin
    Department of Radiology, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu, China.
  • Genji Bai
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • Shuangqing Chen
    Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu Province, 215001, Suzhou City, China. sznaonao@163.com.

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