Frontiers in Oncology (Jan 2025)

Predicting survival rates: the power of prognostic nomograms in distal cholangiocarcinoma

  • Jiangfeng Hu,
  • Yuping Shi,
  • Lihua Jin,
  • Suhong Yi,
  • Jinsuo Chen,
  • Dadong Wan,
  • Weixin Ye,
  • Jingnan Chen,
  • Yajing Zhang,
  • Yang Jiang,
  • Bensong Duan,
  • Yuwei Dong

DOI
https://doi.org/10.3389/fonc.2025.1478836
Journal volume & issue
Vol. 15

Abstract

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ObjectiveThe purpose of this research is to establish a prognostic nomogram for patients with distal cholangiocarcinoma (dCCA).MethodsWe obtained clinical data from 2401 patients diagnosed with distal cholangiocarcinoma (dCCA) between 2010 and 2020 from the Surveillance, Epidemiology, and End Results database. These patients were randomly assigned to either the training or validation group in a ratio of 6:4. 228 patients were enrolled from 9 hospitals in China as the external validation cohort. Univariate and multifactorial Cox regression analyses were conducted to ascertain prognostic factors and prognostic nomograms were developed utilizing LASSO logistic regression analysis. We used the calibration curve, and area under the curve to validate the nomograms. Decision curve analysis was used to evaluate the model and its clinical applicability.ResultsThe findings demonstrated that Grade, M stages, Surgery, and Chemotherapy emerged as autonomous prognostic factors for the survival of individuals with dCCA. The developed nomograms exhibited satisfactory accuracy in forecasting 1-year, 3-year, and 5-year survival probabilities. Furthermore, the calibration curves indicated a strong concordance between the anticipated and observed outcomes. The AUC of the nomogram for 1-year, 3-year, 5 year overall survival (OS) predication were 0.809 (95%CI 78.5-83.3), 0.79 (95%CI 75.8-82.2) and 0.761 ((95%CI 72.3-80.0) in the training cohort, 0.79 (95%CI 75.9-82.0), 0.73 (95%CI 68.5-77.5), and 0.732(95%CI 68.0-78.3) in internal test cohort, 0.862 (95%CI 81.7-90.7),0.83 (95%CI 76.4-89.6),and 0.819(95%CI 74.6-89.2) in external test cohort.ConclusionThe nomograms that have been suggested demonstrate strong predictive capability. These tools can assist medical professionals in assessing the prognosis of patients with dCCA and in devising more accurate treatment strategies for them.

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