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Prediction of hepatocellular carcinoma biological behaviorin patient selection for liver transplantation 被引量:7
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作者 Umberto Cillo Tommaso Giuliani +3 位作者 Marina Polacco Luz Maria Herrero Manley Gino Crivellari Alessandro Vitale 《World Journal of Gastroenterology》 SCIE CAS 2016年第1期232-252,共21页
Morphological criteria have always been considered the benchmark for selecting hepatocellular carcinoma(HCC)patients for liver transplantation(LT).These criteria,which are often inappropriate to express the tumor’s b... Morphological criteria have always been considered the benchmark for selecting hepatocellular carcinoma(HCC)patients for liver transplantation(LT).These criteria,which are often inappropriate to express the tumor’s biological behavior and aggressiveness,offer only a static view of the disease burden and are frequently unable to correctly stratify the tumor recurrence risk after LT.Alpha-fetoprotein(AFP)and its progression as well as AFP-m RNA,AFP-L3%,des-γ-carboxyprothrombin,inflammatory markers and other serological tests appear to be correlated with post-transplant outcomes.Several other markers for patient selection including functional imaging studies such as18F-FDG-PET imaging,histological evaluation of tumor grade,tissue-specific biomarkers,and molecular signatures have been outlined in the literature.HCC growth rate and response to pre-transplant therapies can further contribute to the transplant evaluation process of HCC patients.While AFP,its progression,and HCC response to pretransplant therapy have already been used as a part of an integrated prognostic model for selecting patients,the utility of other markers in the transplant setting is still under investigation.This article intends to review the data in the literature concerning predictors that could be included in an integrated LT selection model and to evaluate the importance of biological aggressiveness in the evaluation process of these patients. 展开更多
关键词 HEPATOCELLULAR CARCINOMA Liver Alphafetoprotein TRANSPLANTATION Biomarkers HISTOPATHOLOGY RECURRENCE Integrated PROGNOSTIC tool
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