ArticleFrontiers in oncology2024
Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.
Article in Frontiers in oncology, 2024. 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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1 citing paper in PubMed.
- CT radiomics of adenocarcinoma of the esophagogastric junction: machine learning-based prediction of perineural invasion status.World journal of surgical oncology · 2026Article
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9 authors.
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Abstract
Background: Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion. Methods: This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve. Results: In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male ( Conclusions: The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.
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