Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study.

BACKGROUND: The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level. METHODS: According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs. RESULTS: Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05). CONCLUSION: The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

A Bayesian network model to predict neoplastic risk for patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

BACKGROUND: Polyp size of 10 mm is insufficient to discriminate neoplastic and non-neoplastic risk in patients with gallbladder polyps (GPs). The aim of the study is to develop a Bayesian network (BN) prediction model to identify neoplastic polyps and create more precise criteria for surgical indications in patients with GPs lager than 10 mm based on preoperative ultrasound features. METHODS: A BN prediction model was established and validated based on the independent risk variables using data from 759 patients with GPs who underwent cholecystectomy from January 2015 to August 2022 at 11 tertiary hospitals in China. The area under receiver operating characteristic curves (AUCs) were used to evaluate the predictive ability of the BN model and current guidelines, and Delong test was used to compare the AUCs. RESULTS: The mean values of polyp cross-sectional area (CSA), long, and short diameter of neoplastic polyps were higher than those of non-neoplastic polyps (P < 0.0001). Independent neoplastic risk factors for GPs included single polyp, polyp CSA ≥ 85 mm 2, fundus with broad base, and medium echogenicity. The accuracy of the BN model established based on the above independent variables was 81.88% and 82.35% in the training and testing sets, respectively. Delong test also showed that the AUCs of the BN model was better than that of JSHBPS, ESGAR, US-reported, and CCBS in training and testing sets, respectively (P < 0.05). CONCLUSION: A Bayesian network model was accurate and practical for predicting neoplastic risk in patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.