Mortality risk after liver transplantation in hepatocellular carcinoma recipients: a nonlinear predictive model

Surgery
Ming ZhangTianfu Wen

Abstract

The balanced application of a model for the estimate of outcomes of liver transplantation, in concert with assessment of disease severity, would not only improve transplant outcomes and maximize patient benefit from transplantation, but also facilitate informed decision making by patients and their relatives when considering transplantation. So far, however, linear discriminating methods have failed to attain sufficient power to predict post-transplant prognosis. Therefore, our aim was to develop a cancer-specific prognostic model by a nonlinear methodology based on pretransplant characteristics. With data collected retrospectively from 290 liver transplant recipients with HCC from February 1999 to August 2009, a multilayer perceptron (MLP) neural network was constructed to predict mortality risk after transplantation. Its predictive performances at posttransplant 1-, 2-, and 5-year intervals were evaluated using a receiver operating characteristic curve. By the forward stepwise selection in MLP network, donor age, donor body mass index, recipient hemoglobin, serum concentrations of total bilirubin, alkaline phosphatase, creatinine, aspartate aminotransferase, international normalized ratio of prothrombin time, and Na(+); alpha...Continue Reading

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Citations

Sep 10, 2013·American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons·C SchuetzH Yeh
May 3, 2014·Seminars in Oncology·Petr Pancoska, Brian I Carr
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Jan 12, 2021·Transplant International : Official Journal of the European Society for Organ Transplantation·Alberto FerrareseMarco Senzolo

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