ArticleDiscover oncology2025
Nomogram models for predicting lymph node metastasis and prognosis in early gastric cancer 60 or older: a population-based cohort study.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Adjuvant chemotherapy and lymph node examination interact to shape survival in older adults (≥ 75 years) with resected gastric cancer: a SEER-based propensity score-matched and competing-risk analysis.World journal of surgical oncology · 2026Article
- Analysis of influencing factors of lymph node metastasis in elderly patients with early gastric cancer and establishment of a prediction model.Discover oncology · 2026Article
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8 authors.
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Abstract
backgroundLymph node metastasis (LNM) is a critical determinant significantly impacting the prognosis of early gastric cancer (EGC).
methodsIn this study, we included 1516 elderly EGC from the Surveillance, Epidemiology, and End Results (SEER) database and 130 patients from China to identify the independent risk factors influencing LNM, overall survival (OS) and cancer-specific survival (CSS) among elderly EGC (60 or older), respectively.
resultsWithin the SEER database, the training cohort revealed that four variables (tumor size, grade, T stage, and examined lymph node count (ELNC)), six variables (age, sex, tumor size, T stage, N stage, and ELNC), and six variables (age, sex, tumor size, tumor location, T stage, and N stage) were identified as essential components in the development of nomograms for LNM, OS, and CSS prediction, respectively. Then, the receiver operating characteristic curve, calibration curve, and decision curve analysis consistently demonstrated the robust predictive capability of these nomograms. Moreover, the application of postoperative adjuvant chemotherapy exhibited a significant positive impact on the survival prognosis of stage Ib patients.
conclusionsThese nomograms for predicting both LNM and survival not only contributed to a deeper comprehension of the underlying biological traits of EGC but also offered a scientific framework to guide treatment decisions.
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