Iwahashi Shuichi, Ghaibeh A Ammar, Shimada Mitsuo, Morine Yuji, Imura Satoru, Ikemoto Tetsuya, Saito Yu, Hirose Jun
Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Mol Clin Oncol. 2020 Nov;13(5):46. doi: 10.3892/mco.2020.2116. Epub 2020 Aug 14.
Hepatocellular carcinoma (HCC) is a highly lethal tumor and the majority of postoperative patients experience recurrence. In the present study, we focus on the predictability of postoperative recurrence on HCC through the data mining method. In total, 323 patients with HCC who underwent hepatic resection were included in the present study, 156 of whom suffered from cancer recurrence. Clinicopathological data including prognosis were analyzed using the data mining method for the predictability of postoperative recurrence on HCC. The resulting alternating decision tree (ADT) was described using data mining method. This tree was validated using a 10-fold cross validation process. The average and standard deviation of the accuracy, sensitivity, and specificity were 69.0±8.2, 59.7±14.5 and 77.7±10.2%, respectively. The identified postoperative recurrence factors were age, viral hepatitis, stage, GOT and T-cholesterol. Data mining method could identify the factors associated at different levels of significance with postoperative recurrence of HCC. These factors could help to predict the postoperative recurrence of HCC.
肝细胞癌（HCC）是一种高致死性肿瘤，大多数术后患者会出现复发。在本研究中，我们通过数据挖掘方法关注HCC术后复发的可预测性。本研究共纳入323例行肝切除术的HCC患者，其中156例出现癌症复发。使用数据挖掘方法分析包括预后在内的临床病理数据，以预测HCC术后复发情况。使用数据挖掘方法描述所得的交替决策树（ADT）。该树通过10倍交叉验证过程进行验证。准确性、敏感性和特异性的平均值及标准差分别为69.0±8.2、59.7±14.5和77.7±10.2%。确定的术后复发因素为年龄、病毒性肝炎、分期、谷草转氨酶和总胆固醇。数据挖掘方法可以识别与HCC术后复发具有不同显著水平相关性的因素。这些因素有助于预测HCC术后复发情况。
