Tanaka Tomohiro, Kurosaki Masayuki, Lilly Leslie B, Izumi Namiki, Sherman Morris
Multiorgan Transplant Program, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Division of Gastroenterology, University Health Netowrk, University of Toronto, Toronto, Ontario, Canada.
J Surg Oncol. 2015 Jul;112(1):72-9. doi: 10.1002/jso.23944. Epub 2015 May 29.
The optimal cutoff of each value in configuring selection criteria for pre-transplant assessment of hepatocellular carcinoma (HCC) remains uncertain.
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).
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).
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.
在为肝细胞癌（HCC）移植前评估配置选择标准时，每个数值的最佳临界值仍不确定。
为构建复发性HCC的预测模型，我们对在大学健康网络接受肝移植（LT）的HCC患者（n = 246）进行了数据挖掘分析。该模型使用来自移植受者科学登记处（SRTR）数据库的队列（n = 9769）进行外部验证。
在246例患者中，14.6%（n = 36）在LT后2.5年内出现复发性HCC。复发性HCC的风险预测模型确定了两个亚组，低风险组（总肿瘤直径[TTD]<4 cm且血清甲胎蛋白[AFP]<73 ng/ml，n = 135）和高风险组（TTD>4 cm和/或AFP>73 ng/ml，n = 111）。该模型的可重复性通过SRTR数据库得到验证；低风险组的总体患者生存率显著高于高风险组（P<0.0001）。使用Cox回归模型，该标准而非米兰标准被证明能有效预测移植后生存，且独立于基础特征（P<0.0001）。
通过数据挖掘分析得出的TTD 4 cm和AFP 73 ng/ml临界值对LT前HCC候选者进行分组，可根据移植后预后有效对患者进行分类。
