Predictive Model for Neoplastic Potential of Gallbladder Polyp.

Journal Article (Journal Article)

GOAL: To provide the statistical predictive model for neoplastic potential of gallbladder polyp (GBP). BACKGROUND: Many studies have attempted to define the risk factors for neoplastic potential of GBP. It remains difficult to precisely adapt the reported risk factors for the decision of surgery. Estimating the probability for neoplastic potential of GBP using a combination of several risk factors before surgical resection would be useful in patient consultation. STUDY: We collected data of patients confirmed as GBP through cholecystectomy at Samsung Medical Center between January 1997 and March 2015. Those with a definite evidence for malignancy, such as adjacent organ invasion, metastasis on preoperative imaging studies, polyp >15 mm, and absence of proper preoperative ultrasonographic imaging were excluded. A total of 1976 patients were enrolled. To make and validate the predictive model, we divided the cohort into the modeling group (n=979) and validation group (n=997). Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. RESULTS: Clinical factors of older age, single lesion, sessile shape, and polyp size showed statistical significance for neoplastic potential of GBP in the modeling group. A predictive model for neoplastic potential of GBP was constructed utilizing the statistical outcome of the modeling group. Statistical validation was performed with the validation group to determine the optimal clinical sensitivity and specificity of the predictive model. Optimal cut-off value for neoplastic probability was 7.4%. CONCLUSIONS: The predictive model for neoplastic potential of GBP may support clinical decisions before cholecystectomy.

Full Text

Duke Authors

Cited Authors

  • Yang, J-I; Lee, JK; Ahn, DG; Park, JK; Lee, KH; Lee, KT; Chi, SA; Jung, S-H

Published Date

  • March 2018

Published In

Volume / Issue

  • 52 / 3

Start / End Page

  • 273 - 276

PubMed ID

  • 28742730

Electronic International Standard Serial Number (EISSN)

  • 1539-2031

Digital Object Identifier (DOI)

  • 10.1097/MCG.0000000000000900


  • eng

Conference Location

  • United States