Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.

Journal: BMC cancer
PMID: 39789538

Abstract

OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

Authors

  • Lei Shen
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Bo Dai
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
  • Shewei Dou
    Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
  • Fengshan Yan
    Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
  • Tianyun Yang
    Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
  • Yaping Wu
    Department of Imaging, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

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