Liver transplantation in the treatment of hepatocellular carcinoma.

We aimed to determine the most appropriate candidates for liver transplantation based on their survival outcomes. Two hundred and fourteen patients who were transplanted in the presence of hepatocellular carcinoma (HCC) were analyzed. Patient groups were selected as "good risk" candidates for transplantation by our previously developed artificial network model or by the classic pTNM pathological classification system. The survival of the model-selected candidate groups was then compared to the survival of the candidates chosen as "good risk" by the pTNM classification (i.e. , pTNM stages I + II and pTNM stages I + II + III). Suitability for transplantation was judged by long-term survival rates (i.e., 1-10 years post-transplant). By using the neural network prediction model and the subsequent subgroup case analysis, it was possible to generate those combinations of risk factors which predetermined patient survival through HCC recurrence. By applying the developed neural network model to the transplant candidate pool for patients with HCC, it was possible to select the maximum number of suitable candidates for transplantation while minimizing donor organ loss to recurrent HCC.