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CONTEXT: Gallbladders resected for non-neoplastic diseases are systemically examined by microscopy to rule out incidental dysplasia and carcinoma.

OBJECTIVE: The main objective was to test if a pre-grossing algorithm can detect incidental gallbladder carcinoma. The secondary objective was to test if the algorithm can detect highgrade dysplasia.

DESIGN: A retrospective study of clinical, pathological, and radiological findings in cholecystectomy recipients was performed on a test set to develop a Classification and Regression Tree (CART) algorithm. Cholecystectomy cases were included, whereas exclusion criteria were age< 18 years old, missing pathology reports, pre-operative suspicion of neoplastic disease and cholecystectomy for non-gallbladder, oncologic disease.

RESULTS: 5663 cholecystectomies from 2006-2018 were included in the study, with 18 cases of incidental gallbladder carcinoma and 11 cases of high-grade dysplasia. 390 controls were randomly selected for the testing set. Patient age, surgical approach, operation duration, dilatation of the biliary tract and gallbladder gross anomalies were statistically significant distinguishing factors at multivariate analysis (P< 0.001-0.026). Unsupervised testing with a conditional inference tree suggested that age, procedure type, and operation duration can identify incidental gallbladder carcinoma from controls, while high-grade dysplasia also requires grossing parameters to identify half of the cases (5/11). CONCLUSION: Readily available clinical parameters and post-operative data can detect incidental gallbladder carcinoma. High-grade dysplasia mostly requires grossing and …