ArticlePeerJ2024
Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.
Article in PeerJ, 2024. 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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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Interpretable deep learning-based hierarchical multi-modal fusion model for predicting HER2 expression in gastric cancer.Frontiers in oncology · 2026Article
- Multi-regional feature integration on enhanced CT for lymph node metastasis prediction in gastric cancer: a novel radiomics approach.BMC medical imaging · 2025Article
- Computed tomography 3D reconstruction and texture analysis for evaluating the efficacy of neoadjuvant chemotherapy in advanced gastric cancer.World journal of gastrointestinal surgery · 2025Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Methods: Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort ( Results: Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. Conclusion: The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.
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