MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.

Journal: Liver international : official journal of the International Association for the Study of the Liver
PMID: 39992060

Abstract

BACKGROUND & AIMS: Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally validate an MRI-based topology DL model for preoperative prediction of MVI.

Authors

  • Tianying Zheng
    Department of Radiology, Functional, and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Yajing Zhu
    12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China.
  • Hanyu Jiang
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Chongtu Yang
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yuxiang Ye
    Diannei Technology, Shanghai, China.
  • Mustafa R Bashir
    Department of Radiology, Duke University, Durham, North Carolina, USA.
  • Chenhui Li
    Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Liling Long
    Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Shishi Luo
    Department of Radiology, Hainan General Hospital, Haikou, Hainan, China.
  • Bin Song
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yinan Chen
    12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China.
  • Yidi Chen
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

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