ArticleBMC cancer2025
Added value of pretreatment CT-based Node-RADS score for predicting survival outcome of locally advanced gastric cancer: compared with clinical N stage.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Development of a Clinicopathological Prognostic Model and Risk Classification to Predict Disease-Free Survival in Patients with Gastric Adenocarcinoma Following Neoadjuvant Chemotherapy and Curative Gastrectomy.Current oncology (Toronto, Ont.) · 2026Article
- Prognostic and Diagnostic Value of Node-RADS for Non-Small Cell Lung Cancer Following Neoadjuvant Therapy: A Multicenter Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Prognostic value of pretherapy CT-based Node-RADS for overall survival in extensive-stage small cell lung cancer treated with chemoimmunotherapy: a comparison with clinical N staging.Quantitative imaging in medicine and surgery · 2026Article
- Pretreatment Claudin-18.2 Expression Predicts Poorer Survival Outcomes in Locally Advanced Gastric Cancer Treated with Perioperative Chemotherapy.Diagnostics (Basel, Switzerland) · 2026Article
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Abstract
objectivesThe Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic value of Node-RADS in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant chemotherapy (NAC) followed by gastrectomy. MATERIALS AND
methodsThis single-center retrospective study included 118 patients with LAGC underwent NAC and gastrectomy. The maximum Node-RADS score and the number of metastatic lymph node stations (defined as LNM-Station) were evaluated on pretreatment CT. The pretreatment Node-RADS-CT and Node-RADS-integrated models were developed using Cox regression to predict overall survival (OS) and disease-free survival (DFS). The pretreatment cN-CT models, cN-integrated models, as well as post-NAC pathological models were also developed in comparison. The performance of the models was assessed in terms of discrimination, calibration and clinical applicability.
resultsThe LNM-Station was significantly associated with OS and DFS (all p < 0.05). The Node-RADS-CT model showed higher Harrell's consistency index (C-index) than cN-CT model (0.755 vs. 0.693 for OS, p = 0.017; 0.759 vs. 0.706 for DFS, p = 0.018). The Node-RADS-integrated model also achieved higher C-index than cN-integrated model (0.771 vs. 0.731 for OS, p = 0.091; 0.773 vs. 0.733 for DFS, p = 0.053). The net reclassification improvement (NRI) of the Node-RADS-integrated model at 5 years was 0.379 for OS and 0.364 for DFS (all p < 0.05). The integrated discrimination improvement (IDI) of the Node-RADS-integrated model was 0.103 for OS and 0.107 for DFS (all p < 0.05). The C-indices (OS: 0.745; DFS: 0.746) of pathological models were slightly lower than those of Node-RADS-based models (all p > 0.05).
conclusionThe baseline Node-RADS score and LNM-Station were effective prognostic indicators for LAGC. The pretreatment CT Node-RADS-based models can offer added prognostic value for LAGC, compared with clinical N stage.
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