Clinical Prediction Models for Recurrence in Patients with Resectable Grade 1 and 2 Sporadic Non-Functional Pancreatic Neuroendocrine Tumors: A Systematic Review

SIMPLE SUMMARY: The risk prediction for tumor recurrence after surgery for non-functional pancreatic neuroendocrine tumors is a major unmet clinical need. Accurate recurrence risk prediction could pave the way for tailor-made follow-up protocols in the future, as well as help select suitable patients for cancer treatment trials. Multiple prediction models have been developed; however, none are currently incorporated into international guidelines. This systematic review found 13 original models, of which 3 were validated outside the patient group in which they were developed. The effectiveness of a prediction model is not proven in the wider population without this validation; thus, the lack of it hinders the progress toward clinical use. We propose to test the included models in a large, multinational database to compare their performance. We recommend all authors developing a prediction model to perform the minimally required tests of model development and to implement it by creating an online calculator. ABSTRACT: Recurrence after resection in patients with non-functional pancreatic neuroendocrine tumors (NF-pNET) has a considerable impact on overall survival. Accurate risk stratification will tailor optimal follow-up strategies. This systematic review assessed available prediction models, including their quality. This systematic review followed PRISMA and CHARMS guidelines. PubMed, Embase, and the Cochrane Library were searched up to December 2022 for studies that developed, updated, or validated prediction models for recurrence in resectable grade 1 or 2 NF-pNET. Studies were critically appraised. After screening 1883 studies, 14 studies with 3583 patients were included: 13 original prediction models and 1 prediction model validation. Four models were developed for preoperative and nine for postoperative use. Six models were presented as scoring systems, five as nomograms, and two as staging systems. The c statistic ranged from 0.67 to 0.94. The most frequently included predictors were tumor grade, tumor size, and lymph node positivity. Critical appraisal deemed all development studies as having a high risk of bias and the validation study as having a low risk of bias. This systematic review identified 13 prediction models for recurrence in resectable NF-pNET with external validations for 3 of them. External validation of prediction models improves their reliability and stimulates use in daily practice.