Published online Feb 7, 2020. doi: 10.3748/wjg.v26.i5.535
Peer-review started: November 7, 2019
First decision: December 7, 2019
Revised: January 6, 2020
Accepted: January 11, 2020
Article in press: January 11, 2020
Published online: February 7, 2020
Processing time: 91 Days and 21.8 Hours
Patients with invasive intraductal papillary mucinous neoplasms (IPMNs) of the pancreas after resection have a distinct unfavorable prognosis. Clinicians need an effective prognostic tool to predict the survival probability of individual patients and to plan further clinical management. To date, no previous study has focused on a predictive model for the prognosis of IPMNs.
Considering the rarity and the indolent course of IPMNs, it is difficult to develop a prognostic nomogram for IPMNs in a single institution. Thus, a prognostic nomogram should be performed based on a population-based cohort with long-term follow-up to achieve the best conclusion. The Surveillance, Epidemiology, and End Results database has provided useful data on prognosis in patients with IPMNs.
We aimed to develop and validate comprehensive nomograms to estimate the probability of long-term overall survival and cancer-specific survival in individual patients with invasive IPMNs of the pancreas who underwent surgical resection.
The information on patients with invasive IPMNs after resection was extracted from the Surveillance, Epidemiology, and End Results database, and then randomly divided into the training and the validation cohorts (roughly 7:3). Based on the Cox regression model, nomograms were constructed to predict the probability of overall survival and cancer-specific survival at different time points for an individual patient. The performance of the nomogram was measured with respect to discrimination, calibration, and clinical utility. Moreover, we compared the predictive accuracy of the nomograms with that of the traditional staging system.
In the training cohort, age, marital status, histological type, T stage, N stage, M stage, and chemotherapy were selected to construct nomograms. Compared to the American Joint Committee on Cancer 7th staging system, the formulated nomograms in this study showed perfect performance with respect to discrimination, calibration, reclassification, and clinical usefulness.
The nomograms showed improved predictive accuracy, discrimination capability, and clinical utility.
These new predictive models need to be validated by a prospective study or at least in another dataset.