Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method

Iwahashi,Shuichi; Ghaibeh,A. Ammar; Shimada,Mitsuo; Morine,Yuji; Imura,Satoru; Ikemoto,Tetsuya; Saito,Yu; Hirose,Jun

doi:10.3892/mco.2020.2116

Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method

Authors:
- Shuichi Iwahashi
- A. Ammar Ghaibeh
- Mitsuo Shimada
- Yuji Morine
- Satoru Imura
- Tetsuya Ikemoto
- Yu Saito
- Jun Hirose
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Affiliations: 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
Published online on: August 14, 2020 https://doi.org/10.3892/mco.2020.2116
Article Number: 46

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Abstract

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.

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