Evidence map›Paper›PMID 32874576›Full record

ArticleMolecular and clinical oncology2020

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

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

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PubMed Full text at PMC Publisher
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Article in Molecular and clinical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shuichi IwahashiDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
A Ammar GhaibehDepartment of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Mitsuo ShimadaDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Yuji MorineDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Satoru ImuraDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Tetsuya IkemotoDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Yu SaitoDepartment of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.
Jun HiroseDepartment of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencedata mining methodhepatocellular carcinomapostoperative recurrenceslternating decision tree

Identifiers

PMID32874576
PMCPMC7453388

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