A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm

Abstract

Objectives: To develop and validate a nomogram for the preoperative prediction of pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) based on multidetector computed tomography (MDCT).

Materials and methods: In this retrospective study, the data of 227 patients with SCN and MCN were analyzed. Each patient underwent MDCT and surgical resection. A multivariable logistic regression model was developed using a training set consisting of 129 patients with SCN and 38 patients with MCN who were admitted between October 2012 and April 2019. The model was validated in 60 consecutive patients, 44 of whom had SCN and 16 of whom had MCN, admitted between May 2019 and April 2020. The regression model was adopted to establish a nomogram. Nomogram performance was determined by its discriminative ability and clinical utility.

Result: The multivariable logistic regression model included sex, size, location, shape, cyst characteristic, and cystic wall thickening. The individualized prediction nomogram showed good discrimination in the training sample (AUC 0.89; 95% CI 0.83-0.95) and in the validation sample (AUC 0.81; 95% CI 0.70-0.94). If the threshold probability is between 0.03 and 0.9, and > 0.93 in the prediction model, using the nomogram to predict SCN and MCN is more beneficial than the treat-all-patients as SCN scheme or the treat-all-patients as MCN scheme. The prediction model showed better discrimination than the radiologists' diagnosis (AUC = 0.68).

Conclusion: The nomogram could predict SCN and MCN preoperatively and may aid clinical decision-making.

Keywords: Multidetector computed tomography; Neoplasms, cystic, mucinous, and serous; Nomograms; Pancreatic neoplasms.