A new strategy to predict the neoplastic polyps of the gallbladder based on a scoring system using EUS.
BACKGROUND
A new method to predict neoplastic polyps of the gallbladder using a scoring system based on five endoscopic ultrasonography (EUS) variables is presented.


METHODS
EUS data from patients with gallbladder polyps who were to undergo cholecystectomy were used for the construction of an EUS scoring system in polyps between 5 and 15 mm in diameter (reference group). The EUS scoring system developed from those patients was applied to other patients (validation group).


RESULTS
In the reference group, size was the most significant predictor of neoplastic polyp. All polyps 5 mm or less in diameter were non-neoplastic and 94% of polyps of greater than 15 mm were neoplastic in the reference group. For polyps between 5 and 15 mm in diameter, the area under the receiver-operating characteristic curves (ROC) plots for the endoscopic scoring system was significantly greater than that under the ROC plots for polyp size alone (p < 0.01). In the validation group, the risk of neoplastic polyp was significantly higher for polyps with a score of 6 or greater compared with those with a score of less than 6 (p < 0.01).


CONCLUSIONS
Our data show that a score based on five EUS variables identifies those patients at risk of neoplasia when polyps are between 5 and 15 mm in diameter. (Gastrointest Endosc 2000;52:372-9).
Choi
W B
WB
Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Lee
S K
SK
Kim
M H
MH
Seo
D W
DW
Kim
H J
HJ
Kim
D I
DI
Park
E T
ET
Yoo
K S
KS
Lim
B C
BC
Myung
S J
SJ
Park
H J
HJ
Min
Y I
YI
D000328:Adult; D003937:Diagnosis, Differential; D019160:Endosonography; D005260:Female; D005706:Gallbladder Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011127:Polyps / Q000000981:diagnostic imaging*; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors