Tomohiro Tanaka1,2, Masayuki Kurosaki3, Leslie B Lilly1,2, Namiki Izumi3, Morris Sherman2. 1. Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada. 2. Division of Gastroenterology, University Health Netowrk, University of Toronto, Toronto, Ontario, Canada. 3. Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Musashino-shi, Tokyo, Japan.
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.
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.
Authors: Michał Grąt; Karolina M Wronka; Jan Stypułkowski; Emil Bik; Maciej Krasnodębski; Łukasz Masior; Zbigniew Lewandowski; Karolina Grąt; Waldemar Patkowski; Marek Krawczyk Journal: Ann Surg Oncol Date: 2016-08-16 Impact factor: 5.344