Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method.

artificial intelligence data mining method hepatocellular carcinoma postoperative recurrence slternating decision tree

Journal

Molecular and clinical oncology
ISSN: 2049-9450
Titre abrégé: Mol Clin Oncol
Pays: England
ID NLM: 101613422

Informations de publication

Date de publication:
Nov 2020
Historique:
received: 28 11 2019
accepted: 27 04 2020
entrez: 3 9 2020
pubmed: 3 9 2020
medline: 3 9 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32874576
doi: 10.3892/mco.2020.2116
pii: MCO-0-0-02116
pmc: PMC7453388
doi:

Types de publication

Journal Article

Langues

eng

Pagination

46

Informations de copyright

Copyright © 2020, Spandidos Publications.

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Auteurs

Shuichi Iwahashi (S)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

A Ammar Ghaibeh (AA)

Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Mitsuo Shimada (M)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Yuji Morine (Y)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Satoru Imura (S)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Tetsuya Ikemoto (T)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Yu Saito (Y)

Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Jun Hirose (J)

Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Classifications MeSH