Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
22
03
2022
revised:
25
04
2022
accepted:
05
05
2022
entrez:
3
6
2022
pubmed:
4
6
2022
medline:
7
6
2022
Statut:
epublish
Résumé
Computer tomography texture analysis (CTTA) based on the V-Net convolutional neural network (CNN) algorithm was used to analyze the recurrence of advanced gastric cancer after radical treatment. Meanwhile, the clinical characteristics of patients were analyzed to explore the recurrence factors. 86 patients who underwent the advanced radical gastrectomy for gastric cancer were retrospectively selected as the research objects. Patients were divided into the no-recurrence group (30 cases) and the recurrence group (56 cases) according to whether there was recurrence after radical treatment. CTTA was performed before and after surgery in both groups to analyze the risk factors for recurrence. The results showed that the dice coefficient (0.9209) and the intersection over union (IOU) value (0.8392) of the V-CNN segmentation effect were signally higher than those of CNN, V-Net, and context encoder network (CE-Net) (
Identifiants
pubmed: 35655504
doi: 10.1155/2022/1852718
pmc: PMC9155975
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1852718Informations de copyright
Copyright © 2022 Zhiwu Zhou et al.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
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