JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Development and Validation of a Preoperative Prediction Model for Neoplastic Gallbladder Polyps

  • 0Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
British Journal of Hospital Medicine +

|

February 25, 2025

|

February 25, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a practical prediction model to identify neoplastic polyps (NP) in gallbladder polypoid lesions (GPLs) using clinical data. The model accurately predicts NP, aiding in clinical decision-making for GPL management.

Area Of Science

  • Gastroenterology and Hepatology
  • Surgical Oncology
  • Medical Imaging

Background

  • Gallbladder polypoid lesions (GPLs) require evaluation to distinguish neoplastic polyps (NP) from benign ones.
  • Accurate preoperative identification of NP is crucial for appropriate patient management and surgical planning.
  • Existing risk factor studies for NP have not resulted in a practical predictive model.

Purpose Of The Study

  • To develop and validate a practical preoperative prediction model for neoplastic polyps (NP) in gallbladder polypoid lesions (GPLs).
  • To utilize simple and easily accessible clinical variables for predicting NP.
  • To improve clinical decision-making in the management of GPLs.

Main Methods

  • Retrospective analysis of 621 patients with GPLs who underwent cholecystectomy.
  • Development of a logistic regression model incorporating age, polyp size, polyp number, gallbladder wall thickness, and polyp echo characteristics.
  • Validation of the model using training, internal, and external datasets with assessment of discrimination, calibration, and clinical utility.

Main Results

  • Key predictors for NP included age, polyp size, polyp number, gallbladder wall thickness, and polyp echo characteristics.
  • The nomogram model achieved high predictive accuracy with AUCs of 0.886 (training), 0.836 (internal validation), and 0.867 (external validation).
  • The model demonstrated good calibration across all datasets and significant clinical benefit at a threshold probability of 0.6.

Conclusions

  • A practical preoperative prediction model incorporating accessible clinical variables effectively identifies neoplastic polyps in gallbladder polypoid lesions.
  • The developed nomogram model shows excellent performance and aids in clinical decision-making for GPL management.
  • This tool can assist clinicians in stratifying risk and determining the optimal management strategy for patients with GPLs.

Keywords: