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Ya-Qi Zhai

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Open access Jun 2026

A practical nomogram for predicting survival in patients with extrahepatic cholangiocarcinoma: a population-based cohort study

Background Extrahepatic cholangiocarcinoma (ECC) is a highly aggressive malignancy. This study aimed to develop and validate a comprehensive prognostic nomogram incorporating treatment data to enable individualized risk stratification and refine patient selection for palliative interventions. Methods Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) program (for the period from 2018 to 2022). There were 2,254 patients identified and randomly divided into model development (n=1,577) and validation (n=677) cohorts. Prognostic factors were selected through multivariate Cox regression using backward elimination based on the Akaike Information Criterion (AIC). A nomogram was developed to estimate 6-month, 1-year, and 3-year survival probabilities. Model performance was evaluated using the consistency index (C-index), calibration curves, and decision curve analysis (DCA). Finally, risk stratification cutoffs were determined to facilitate individualized prognostic assessment in palliative settings. Results The final nomogram incorporated age, N stage, M stage, surgery, and chemotherapy status. The model demonstrated good discrimination, with C-indices of 0.770 [95% confidence interval (CI): 0.750–0.790] in the training cohort and 0.789 (95% CI: 0.768–0.802) in the validation cohort. Calibration curves indicated good agreement between predicted and observed probabilities. DCA revealed good net benefit across clinically relevant threshold probabilities. Risk stratification identified high-risk (<3 months, score ≥70), intermediate-risk (3–12 months, score 59–69), and low-risk (>1 year, score ≤58) groups, potentially guiding the selection between plastic and metal stents in palliative settings. Conclusions This prognostic model provides accurate individualized survival prediction for ECC and facilitates objective risk stratification.

Guan-Jun Zhang, Ya-Qi Zhai, Ke Meng et al. · 0 citations

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