Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis

Background and Objectives The optimal cutoff of each value in configuring selection criteria for pre‐transplant assessment of hepatocellular carcinoma (HCC) remains uncertain. Methods To build a predictive model for recurrent HCC, we performed data mining analysis on patients who underwent LT for HCC at University Health Network (n = 246). The model was externally validated using a cohort from the Scientific Registry of Transplant Recipients (SRTR) database (n = 9,769). Results Among 246 patients, 14.6% (n = 36) experienced recurrent HCC within 2.5 years post‐LT. The risk prediction model for recurrent HCC identified two subgroups with low‐risk (total tumor diameter [TTD] <4 cm and serum alpha‐fetoprotein [AFP] <73 ng/ml, n = 135) and with high‐risk (TTD >4 cm and/or AFP >73 ng/ml, n = 111). The reproducibility of the model was validated through the SRTR database; overall patient survival rate was significantly better in low‐risk group than high‐risk group ( P  < 0.0001). Using Cox regression model, this yardstick, not Milan criteria, was revealed to efficiently predict post‐transplant survival independent of underlying characteristics ( P  < 0.0001). Conclusions Grouping LT candidates with pre‐LT HCC by the cutoffs of TTD 4 cm and AFP 73 ng/ml which were unearthed by data mining analysis efficiently classify patients according by the post‐transplant prognosis. J. Surg. Oncol. 2015 111:72–79 . © 2015 Wiley Periodicals, Inc.