Nomograms for predicting survival outcomes in patients with neuroendocrine neoplasms of the gallbladder undergoing primary tumor resection: a population-based study

Background: Neuroendocrine neoplasms of the gallbladder (GB-NENs) are a rare group of histologically heterogeneous tumors, and surgical resection of the primary tumor is the mainstream treatment at the moment. The current study aimed to establish and validate novel nomograms for patients with GB-NENs undergoing primary tumor resection to predict the 6-, 12-, and 18- months overall survival (OS) and cancer-specific survival (CSS). Methods Clinicopathological information of patients with GB-NENs undergoing primary tumor resection between 2004 and 2018 was derived from the Surveillance, Epidemiology, and End Results (SEER) database. The cohort was then randomly assigned to the training and internal validation datasets (7:3). Candidate prognostic factors were selected by Cox regression analyses, and the nomograms were constructed. Finally, concordance index (C-index), calibration plot, and area under the curve from the receiver operating characteristic curve (AUC), were conducted to assess the effective performance of the nomograms. The decision curve analysis (DCA) was employed to further confirm the clinical effectiveness of nomograms. To test the generalizability of the prediction models, we also included 12 patients with GB-NENs undergoing resection from Wuhan Union Hospital. Results A total of 221 patients with GB-NENs undergoing resection were enrolled in our study. Using the Cox regression analyses, age, tumor size, and stage were identified as the independent prognostic factors of patients with GB-NENs undergoing resection. We constructed individualized nomograms to visually predict the OS and CSS in patients with GB-NENs undergoing resection. The C-indexes of OS and CSS in training dataset were 0.798 (95%CI: 0.748–0.848) and 0.825 (95%CI:0.771–0.879), while that of internal validation dataset was 0.849 (95%CI: 0.791–0.907) and 0.875 (95%CI: 0.819–0.931), respectively. The AUC of the training and internal validation datasets also indicated that the prediction models performed accurately. The calibration curves demonstrated that the observed survival probability of nomograms was extremely compatible with the predicted probability. The DCA showed that our nomograms were clinically useful. Conclusions Taken together, the nomograms are accurate enough to predict the prognostic factors of GB-NENs patients undergoing resection, allowing for treatment decision-making and clinical monitoring for future clinical work.