title: Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis.
abstract: OBJECTIVE
  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.
authors.affiliation: Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada.|Division of Gastroenterology, University Health Netowrk, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Tomohiro
authors.initials: T
authors.lastname: Tanaka
authors.affiliation: Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Musashino-shi, Tokyo, Japan.
authors.forename: Masayuki
authors.initials: M
authors.lastname: Kurosaki
authors.affiliation: Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada.|Division of Gastroenterology, University Health Netowrk, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Leslie B
authors.initials: LB
authors.lastname: Lilly
authors.affiliation: Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Musashino-shi, Tokyo, Japan.
authors.forename: Namiki
authors.initials: N
authors.lastname: Izumi
authors.affiliation: Division of Gastroenterology, University Health Netowrk, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Morris
authors.initials: M
authors.lastname: Sherman
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D057225:Data Mining / Q000379:methods*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008113:Liver Neoplasms / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D016031:Liver Transplantation / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D009367:Neoplasm Staging; D018579:Patient Selection; D011379:Prognosis; D012042:Registries; D012189:Retrospective Studies; D015996:Survival Rate
keywords: Milan criteria; data mining; decision tree analysis; hepatocellular carcinoma; liver transplantation