ArticleScientific reports2026
Clinicopathological characteristics and prognostic model validation for mucinous gastric carcinoma.
Article in Scientific reports, 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
This study aimed to investigate the clinicopathological characteristics, survival outcomes, and to construct and validate a prognostic nomogram for mucinous gastric carcinoma (MGC) using a population-based cohort. Data were obtained from the SEER database (2000–2021) for patients diagnosed with MGC. Clinicopathological characteristics, including age, gender, tumor location, size, grade, treatment modalities, and survival outcomes, were analyzed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors, and a nomogram was developed. Model performance was evaluated using C-index, calibration plots, ROC curve analysis, and decision curve analysis. A total of 719 patients diagnosed with MGC were included from the SEER database. The median OS was 20 months, and the median CSS was 27 months. Eligible patients were randomly divided into a training cohort and a validation cohort in a 7:3 ratio. Univariate analysis revealed that multiple clinicopathological factors were significantly associated with OS. The final independent prognostic factors for OS included age, income, T stage, N stage, M stage, tumor size, diagnosis-to-treatment interval, surgery, and chemotherapy. A prognostic nomogram was constructed based on these variables. The concordance index for OS prediction was 0.721 in the training cohort and 0.717 in the validation cohort. The area under the curve values for 1-, 3- and 5-year OS predictions were 0.808, 0.784 and 0.782 in the training cohort, and 0.760, 0.797 and 0.787 in the validation cohort, respectively. Calibration curves demonstrated good agreement between predicted and observed outcomes. DCA and clinical impact curves indicated that the nomogram provided clinical benefit across a range of risk thresholds. The developed nomogram provides an individualized tool for predicting survival in MGC patients, offering a more accurate prognostic method than traditional staging systems. Incorporate with additional genetic markers and clinical factors might enhance its prognostic value in the future.
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