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Predictive and prognostic models and visualizations of distant metastasis in gallbladder cancer

  • 0Department of Colorectal Hernia Surgery, Binzhou Medical University Hospital, Binzhou, Shandong Province, China.
Medicine +

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July 23, 2025

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July 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed accurate nomograms to predict distant metastasis (DM) risk and overall survival (OS) in gallbladder cancer (GBC) patients. These tools identified key factors like race, stage, and tumor size for DM and age, surgery, and chemotherapy for OS, aiding clinical decisions.

Area Of Science

  • Oncology
  • Biostatistics
  • Cancer Epidemiology

Background

  • Gallbladder cancer (GBC) poses a significant health challenge with complex metastatic patterns and variable survival outcomes.
  • Accurate prediction of distant metastasis (DM) and overall survival (OS) is crucial for effective GBC management and patient counseling.

Purpose Of The Study

  • To develop and validate predictive models, visualized as nomograms, for estimating the risk of DM and predicting OS in GBC patients.
  • To identify independent risk factors for DM and prognostic factors for OS in GBC.

Main Methods

  • Utilized multivariate statistical methods on data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database (2010-2015).
  • Employed logistic regression for DM risk prediction and Cox regression for OS prognosis.
  • Validated model performance using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis.

Main Results

  • Identified race, T stage, N stage, and tumor size as independent predictors of DM in GBC.
  • Found age, surgical intervention, and chemotherapy to be independent prognostic factors for OS in GBC patients with DM.
  • Nomograms demonstrated high accuracy, with Area Under the Curve (AUC) values around 0.73 for DM prediction and favorable performance for OS prediction at various time points (6, 9, 12 months).

Conclusions

  • Successfully developed and validated accurate nomograms for predicting DM occurrence and OS in GBC patients.
  • These nomograms offer valuable clinical utility for risk stratification and prognosis assessment in GBC management.
  • The identified factors provide insights into GBC progression and inform personalized treatment strategies.

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