ArticleComputational intelligence and neuroscience2022
Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Differentiating small intestinal stromal tumors from primary small intestinal lymphomas using contrast-enhanced CT and texture analysis: a diagnostic study.Frontiers in oncology · 2025Article
- Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer.Frontiers in oncology · 2022Article
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6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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) (
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