Department of Digestive Disease Information and Research and Department of Medicine, Kurume University School of Medicine, Kurume.
Hepatol Res. 2011 Jun;41(6):564-71. doi: 10.1111/j.1872-034X.2011.00799.x. Epub 2011 Apr 19.
Non-hepatitis B virus/non-hepatitis C virus-related hepatocellular carcinoma (NBNC-HCC) is often detected at an advanced stage, and the pathology associated with the staging of NBNC-HCC remains unclear. Data mining is a set of statistical techniques which uncovers interactions and meaningful patterns of factors from a large data collection. The aims of this study were to reveal complex interactions of the risk factors and clinical feature profiling associated with the staging of NBNC-HCC using data mining techniques.
A database was created from 663 patients with NBNC-HCC at 20 institutions. The Milan criteria were used as staging of HCC. Complex associations of variables and clinical feature profiling with the Milan criteria were analyzed by graphical modeling and decision tree algorithm methods, respectively.
Graphical modeling identified six factors independently associated with the Milan criteria: diagnostic year of HCC; diagnosis of liver cirrhosis; serum aspartate aminotransferase (AST); alanine aminotransferase (ALT); α-fetoprotein (AFP); and des-γ-carboxy prothrombin (DCP) levels. The decision trees were created with five variables to classify six groups of patients. Sixty-nine percent of the patients were within the Milan criteria, when patients showed an AFP level of 200 ng/mL or less, diagnosis of liver cirrhosis and an AST level of less than 93 IU/mL. On the other hand, 18% of the patients were within the Milan criteria, when patients showed an AFP level of more than 200 ng/mL and ALT level of 20 IU/mL or more.
Data mining disclosed complex interactions of the risk factors and clinical feature profiling associated with the staging of NBNC-HCC.
非乙型肝炎病毒/非丙型肝炎病毒相关性肝细胞癌（NBNC-HCC）常发现于晚期，且与 NBNC-HCC 分期相关的病理学仍不清楚。数据挖掘是一套统计技术，可从大量数据集中发现因素之间的相互作用和有意义的模式。本研究旨在利用数据挖掘技术揭示与 NBNC-HCC 分期相关的危险因素和临床特征分析的复杂相互作用。
从 20 家机构的 663 例 NBNC-HCC 患者中创建了一个数据库。采用米兰标准进行 HCC 分期。通过图形建模和决策树算法方法分别分析变量和临床特征分析与米兰标准的复杂关联。
图形建模确定了与米兰标准独立相关的六个因素：HCC 的诊断年份；肝硬化的诊断；血清天冬氨酸转氨酶（AST）；丙氨酸转氨酶（ALT）；甲胎蛋白（AFP）；和去γ-羧基凝血酶原（DCP）水平。决策树由五个变量创建，用于将患者分为六组。当患者 AFP 水平≤200ng/mL、诊断为肝硬化和 AST 水平<93IU/mL 时，69%的患者符合米兰标准。另一方面，当患者 AFP 水平>200ng/mL 和 ALT 水平≥20IU/mL 时，18%的患者符合米兰标准。
数据挖掘揭示了与 NBNC-HCC 分期相关的危险因素和临床特征分析的复杂相互作用。
