Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

Journal: World journal of surgical oncology
PMID: 35676669

Abstract

BACKGROUND: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.

Authors

  • Bao-Ye Sun
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
  • Pei-Yi Gu
    School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
  • Ruo-Yu Guan
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
  • Cheng Zhou
    Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Jian-Wei Lu
    School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
  • Zhang-Fu Yang
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
  • Chao Pan
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
  • Pei-Yun Zhou
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
  • Ya-Ping Zhu
    School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
  • Jia-Rui Li
    School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
  • Zhu-Tao Wang
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
  • Shan-Shan Gao
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China.
  • Wei Gan
    College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China.
  • Yong Yi
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China. yi.yong@zs-hospital.sh.cn.
  • Ye Luo
    School of Software Engineering, Tongji University, Shanghai, People's Republic of China. yeluo@tongji.edu.cn.
  • Shuang-Jian Qiu
    Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China. qiu.shuangjian@zs-hospital.sh.cn.

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