Published online Mar 28, 2025. doi: 10.3748/wjg.v31.i12.100855
Revised: January 10, 2025
Accepted: February 18, 2025
Published online: March 28, 2025
Processing time: 209 Days and 23.8 Hours
Occult pancreaticobiliary reflux (OPBR) is characterized by the absence of congenital anomalies at the pancreaticobiliary junction yet leads to altered bile composition and an increased incidence of gallbladder stones.
To explore the computed tomography (CT) imaging characteristics of gallbladder stones in patients diagnosed with OPBR.
We analyzed 362 patients undergoing cholecystectomy (November 2020 to January 2022). Intraoperative bile samples were assayed for amylase (> 110 U/L indicated OPBR). CT features, including stone density and visibility, were compared between 54 OPBR and 308 controls. Stone attenuation (HU) was measured under standardized conditions (uCT-780, 120 kVp, 160 mAs). Logistic regression and receiver operating characteristic curve analysis identified independent OPBR predictors, forming a validated nomogram.
OPBR patients exhibited significantly higher rates of CT-invisible stones (35.2% vs 12.3%) and uniform stones (87% vs 73.1%) along with lower overall stone density
CT imaging distinctly identifies gallbladder stone density, indicating a heightened risk of OPBR in patients with uniform and CT-invisible stones. Such imaging is crucial for preoperative assessments to evaluate potential recurrent biliary pathologies post-cholecystectomy.
Core Tip: This study presented a novel approach to diagnosing occult pancreaticobiliary reflux in patients with gallbladder stones using non-invasive computed tomography (CT) imaging. The findings highlighted that uniform and CT-invisible stones, along with lower stone density, are significant predictors of occult pancreaticobiliary reflux. The development of a predictive nomogram based on these imaging features offers clinicians an effective tool for early identification and intervention, potentially reducing the risk of recurrent biliary pathologies post-cholecystectomy. This innovative diagnostic model may enhance patient management and improve clinical outcomes in gallbladder disease.