Computed tomography nomogram to predict a high-risk intraductal papillary mucinous neoplasm of the pancreas

The intraductal papillary mucinous neoplasm (IPMN) of the pancreas is regarded as a precursor to pancreatic cancer; this study aimed to develop and validate a model based on CT characteristics for the non-invasive prediction of the high-risk IPMN of the pancreas.

In this retrospective study, all patients underwent multidetector CT and surgical resection. A prediction model was developed based on a training set consisting of 136 patients with low-risk IPMN and 85 patients with high-risk IPMN between October 2012 and April 2019, and a multivariable logistic regression model was adopted to establish a nomogram. The nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 80 consecutive patients between May 2019 and April 2020, of which 47 and 33 patients had low-risk and high-risk IPMNs, respectively.

The multivariable logistic regression model of CT characteristics included the enhancing mural nodule size, the main pancreatic duct (MPD) diameter, the abrupt change in caliber of the MPD with distal pancreatic atrophy, cyst size, thickened enhancing cyst wall, and the presence of lymphadenopathy. The individualized prediction nomogram using these predictors of the high-risk IPMN achieved an area under the curve (AUC) of 0.92 (95% CI 0.88-0.95) in the training set and 0.87 (95% CI 0.79-0.95) in the validation set. The decision curve analysis demonstrated that the nomogram was clinically useful.

The CT nomogram, which is a non-invasive predictive tool, can preoperatively predict the risk of malignant IPMN and help identify patients who require a surgical procedure.