ArticleDiscover oncology2026
An individualized prognostic model for survival prediction in gastric-type endocervical adenocarcinoma.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeGastric-type endocervical adenocarcinoma (G-EAC) is a rare but highly aggressive malignant tumor. This study aimed to develop and validate a prognostic nomogram to accurately predict overall survival (OS) for patients with G-EAC.
methodsA total of 385 patients with G-EAC were included, comprising 346 from the SEER database and 39 patients from our hospital as an external validation set. The SEER cohort was randomly split in a 7:3 ratio into a development set (n = 241) and an internal validation set (n = 105). Predictive factors were identified in the development set using univariate and multivariate Cox regression analyses, and a model for OS was constructed and visualized as a nomogram. The predictive performance of the model was evaluated using concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUROC) in both internal and external validation.
resultsThe final prognostic model included six independent predictors: age at diagnosis, tumor stage, T stage, tumor size, primary site surgery, and chemotherapy. The C-index values were 0.88 (95% CI 0.81–0.96), 0.88 (95% CI 0.76–0.99) and 0.87 (95% CI 0.69–1.00) for the development set, internal validation and external validation set, respectively. In the development set, the time-dependent AUROCs for 1-, 3-, and 5-year OS were 0.94 (95% CI: 0.90–0.98), 0.93 (95% CI: 0.89–0.99), and 0.92 (95% CI: 0.87–0.97), respectively. The corresponding values in the internal validation set were 0.98 (95% CI: 0.95–1.00), 0.92 (95% CI: 0.88–0.98), and 0.91 (95% CI: 0.83–0.98), and in the external validation cohort were 0.97 (95% CI: 0.92–1.00), 0.77 (95% CI: 0.53–0.99), and 0.68 (95% CI: 0.32–1.00).
conclusionWe developed and validated a nomogram with good discriminative ability for predicting OS in patients with G-EAC. This tool may assist clinicians in identifying high-risk patients and guiding individualized treatment decisions.
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