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

Tomohiro Tanaka; Masayuki Kurosaki; Leslie B Lilly; Namiki Izumi; Morris Sherman

doi:10.1002/jso.23944

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

Journal article

Peer reviewed

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

Tomohiro Tanaka, Masayuki Kurosaki, Leslie B Lilly, Namiki Izumi and Morris Sherman

Journal of surgical oncology, Vol.112(1), pp.72-79

07/2015

DOI: 10.1002/jso.23944

PMID: 26032085

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Abstract

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.

Data Mining

Milan criteria

decision tree analysis

liver transplantation

hepatocellular carcinoma

Details

Title: Subtitle: Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis
Creators: Tomohiro Tanaka - Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada
Masayuki Kurosaki - Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Musashino-shi, Japan
Leslie B Lilly - Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada
Namiki Izumi - Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Musashino-shi, Japan
Morris Sherman - Division of Gastroenterology, University Health Netowrk, University of Toronto, Ontario, Toronto, Canada
Resource Type: Journal article
Publication Details: Journal of surgical oncology, Vol.112(1), pp.72-79
DOI: 10.1002/jso.23944
PMID: 26032085
NLM abbreviation: J Surg Oncol
ISSN: 0022-4790
eISSN: 1096-9098
Publisher: Blackwell Publishing Ltd
Number of pages: 8
Language: English
Date published: 07/2015
Academic Unit: Gastroenterology and Hepatology; Internal Medicine
Record Identifier: 9984094368302771

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