Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence

作     者：Quirino Lai Carmine De Stefano Jean Emond Prashant Bhangui Toru Ikegami Benedikt Schaefer Maria Hoppe-Lotichius Anna Mrzljak Takashi Ito Marco Vivarelli Giuseppe Tisone Salvatore Agnes Giuseppe Maria Ettorre Massimo Rossi Emmanuel Tsochatzis Chung Mau Lo Chao-Long Chen Umberto Cillo Matteo Ravaioli Jan Paul Lerut the EurHeCaLT and the West-East LT Study Group 

作者机构：General Surgery and Organ Transplantation Unit AOU Policlinico Umberto I Sapienza University of Rome Rome Italy Data Science and Engineering Polytechnic of Turin Turin Italy Division of Liver Transplantation and Hepatobiliary Surgery Department of Surgery Weill Cornell Medicine-Columbia University New York US Medanta Institute of Liver Transplantation and Regenerative Medicine Medanta-The Medicity Gurgaon India Department of Surgery and Science Kyushu University Fukuoka Japan Department of Medicine I Gastroenterology Hepatology and Endocrinology Medical University of Innsbruck Innsbruck Austria Klinik für Allgemein- Viszeral- und Transplantationschirurgie Universitätsmedizin Mainz Mainz Germany Liver Transplant Centre Merkur University of Zagreb Zagreb Croatia Division of Hepato-Biliary-Pancreatic and Transplant Surgery Department of Surgery Graduate School of Medicine Kyoto Japan Unit of Hepatobiliary Surgery and Transplantation AOU Ospedali Riuniti Polytechnic University of Marche Ancona Italy Department of Surgical Sciences and Medical Sciences University of Rome-Tor Vergata Rome Italy Liver Unit Department of Surgery Catholic University-Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Department of Transplantation and General Surgery San Camillo Hospital Rome Italy UCL Institute for Liver and Digestive Health and Royal Free Sheila Sherlock Liver Centre Royal Free Hospital London UK Hong Kong University–Department of Surgery Queen Mary Hospital University of Hong Kong Hong Kong P. R. China Department of Surgery Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan P. R. China Department of Surgery Oncology and Gastroenterology University of Padua Padua Italy Department of General Surgery and Transplantation IRCCS Azienda Ospedaliero-Universitaria di Bologna Bologna University Bologna Italy Institut de Recherche Clinique Université catholique de Louvain Brussels Belgium 不详 

出 版 物：《Cancer Communications》 (癌症通讯（英文）)

年 卷 期：2023年第43卷第12期

页      面：1381-1385页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100214[医学-肿瘤学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：hepatocellular cancer artificial 

摘      要：Dear Editor,In recent years,criteria based on the combinationof morphology and biology have been proposed forimproving the selection of hepatocellular cancer(HCC)patients waiting for liver transplantation(LT)[1,2].Since all the proposed models showed suboptimalresults in predicting the risk of postLT recurrence,aprediction model constructed using artificial intelligence(Al)could be an attractive way to surpass this limit[3,4].Therefore,the Time_Radiological-response_Alpha-fetoproteIN_Artificial-Intelligence(TRAIN-AI)modelwas developed,combining morphology and biology tumorvariables.