Abstract
BACKGROUND: Despite the low recurrence rate of resected nonfunctional pancreatic neuroendocrine tumors (NF-pNETs), nearly all patients undergo long-term surveillance. A prediction model for recurrence may help select patients for less intensive surveillance or identify patients for adjuvant therapy. The objective of this study was to assess the external validity of a recently published model predicting recurrence within 5 years after surgery for NF-pNET in an international cohort. This prediction model includes tumor grade, lymph node status and perineural invasion as predictors. METHODS: Retrospectively, data were collected from 7 international referral centers on patients who underwent resection for a grade 1-2 NF-pNET between 1992 and 2018. Model performance was evaluated by calibration statistics, Harrel's C-statistic, and area under the curve (AUC) of the receiver operating characteristic curve for 5-year recurrence-free survival (RFS). A sub-analysis was performed in pNETs >2 cm. The model was improved to stratify patients into 3 risk groups (low, medium, high) for recurrence. RESULTS: Overall, 342 patients were included in the validation cohort with a 5-year RFS of 83% (95% confidence interval [CI]: 78-88%). Fifty-eight patients (17%) developed a recurrence. Calibration showed an intercept of 0 and a slope of 0.74. The C-statistic was 0.77 (95% CI: 0.70-0.83), and the AUC for the prediction of 5-year RFS was 0.74. The prediction model had a better performance in tumors >2 cm (C-statistic 0.80). CONCLUSIONS: External validity of this prediction model for recurrence after curative surgery for grade 1-2 NF-pNET showed accurate overall performance using 3 easily accessible parameters. This model is available via www.pancreascalculator.com.
Original language | English |
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Pages (from-to) | 571-579 |
Number of pages | 9 |
Journal | Neuroendocrinology |
Volume | 112 |
Issue number | 6 |
DOIs | |
Publication status | Published - 1 May 2022 |
Keywords
- Nonfunctional pancreatic neuroendocrine tumors
- Prediction model
- Recurrence
- Risk factors