Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.

Journal: Surgery
PMID:

Abstract

BACKGROUND: Posthepatectomy liver failure is a worrisome complication after major hepatectomy for hepatocellular carcinoma and is the leading cause of postoperative mortality. Recommendations for hepatectomy for hepatocellular carcinoma are based on the risk of severe posthepatectomy liver failure, and accurately predicting posthepatectomy liver failure risk before undertaking major hepatectomy is of great significance. Thus, herein, we aimed to establish and validate an artificial neural network model to predict severe posthepatectomy liver failure in patients with hepatocellular carcinoma who underwent hemihepatectomy.

Authors

  • Rong-Yun Mai
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Department of Experimental Research, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China.
  • Hua-Ze Lu
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China.
  • Tao Bai
    Department of Infectious Disease, Wuhan Jinyintan Hospital, Wuhan, Hubei 430048, China.
  • Rong Liang
    Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China; Department of First Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning, China.
  • Yan Lin
  • Liang Ma
    College of Information and Management, National University of Defense Technology, Changsha 410073, China.
  • Bang-de Xiang
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China.
  • Guo-Bin Wu
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China.
  • Le-Qun Li
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China.
  • Jia-Zhou Ye
    Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning, China; Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China. Electronic address: yejiazhou2019@163.com.