An individualized prognostic model for survival prediction in gastric-type endocervical adenocarcinoma
Year: 2026
Type: article
Study design: Observational Study
Abstract: PURPOSE: Gastric-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. METHODS: A 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. RESULTS: The 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). CONCLUSION: We 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.
Source: Discover Oncology
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,690
OpenAlex ID: W7128715171