Development and Validation of a Multi-institutional Preoperative Nomogram for Predicting Grade of Dysplasia in Intraductal Papillary Mucinous Neoplasms (IPMNs) of the Pancreas: A Report from The Pancreatic Surgery Consortium

Objective: Previous nomogram models for patients undergoing resection of intraductal papillary mucinous neoplasms (IPMNs) have been relatively small single-institutional series. Our objective was to improve upon these studies by developing and independently validating a new model using a large multiinstitutional dataset. Summary Background Data: IPMNs represent the most common radiographically identifiable precursor lesions of pancreatic cancer. They are a heterogenous group of neoplasms in which more accurate markers of high-grade dysplasia or early invasive carcinoma could help avoid unnecessary surgery in 1 case and support potentially curative intervention (resection) in another. Methods: Prospectively maintained databases from 3 institutions were queried for patients who had undergone resection of IPMNs between 2005 and 2015. Patients were separated into main duct [main and mixed-type (MD)] and branch duct (BD) types based on preoperative imaging. Logistic regression modeling was used on a training subset to develop 2 independent nomograms (MD and BD) to predict low-risk (low- or intermediate-grade dysplasia) or high-risk (high-grade dysplasia or invasive carcinoma) disease. Model performance was then evaluated using an independent validation set. Results: We identified 1028 patients who underwent resection for IPMNs [MD: n = 454 (44%), BD: n = 574 (56%)] during the 10-year study period. High-risk disease was present in 487 patients (47%). Patients with high-risk disease comprised 71% and 29% of MD and BD groups, respectively (P 3.0 cm, solid component/mural nodule, pain symptoms, and weight loss were significantly associated with high-risk disease. C-indices were 0.82 and 0.81 on training and independent validation sets, respectively; Brier scores were 0.173 and 0.175, respectively. Conclusions: For patients with suspected IPMNs, we present an independently validated model for the prediction of high-risk disease.

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