Chen Jeffrey W, Heidsma Charlotte M, Engelsman Anton F, Kabaktepe Ertunç, van Dieren Susan, Falconi Massimo, Besselink Marc G, Nieveen van Dijkum Els J M
Department of Surgery, Amsterdam UMC, Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands.
Amsterdam Center for Endocrine and Neuroendocrine Tumors (ACcENT), 1081 HV Amsterdam, The Netherlands.
Cancers (Basel). 2023 Feb 28;15(5):1525. doi: 10.3390/cancers15051525.
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 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.
无功能性胰腺神经内分泌肿瘤（NF-pNET）患者切除术后复发对总生存期有相当大的影响。准确的风险分层将制定最佳的随访策略。本系统评价评估了现有的预测模型，包括其质量。本系统评价遵循PRISMA和CHARMS指南。截至2022年12月，检索了PubMed、Embase和Cochrane图书馆，以查找针对可切除的1级或2级NF-pNET复发开发、更新或验证预测模型的研究。对研究进行了严格评价。在筛选了1883项研究后，纳入了14项研究共3583例患者：13项原始预测模型和1项预测模型验证。4项模型用于术前，9项用于术后。6项模型以评分系统形式呈现，5项以列线图形式呈现，2项以分期系统形式呈现。统计量范围为0.67至0.94。最常纳入的预测因素是肿瘤分级、肿瘤大小和淋巴结阳性。严格评价认为所有开发研究存在高偏倚风险，验证研究存在低偏倚风险。本系统评价确定了13项可切除NF-pNET复发的预测模型，其中3项有外部验证。预测模型的外部验证提高了其可靠性并促进了在日常实践中的应用。
