Intraductal Papillary Mucinous Neoplasms in High-Risk Individuals: Incidence, Growth Rate, and Malignancy Risk
Bibliographic record
Abstract
BACKGROUND AND AIMS: In high-risk individuals (HRIs), we aimed to assess the cumulative incidence of intraductal papillary mucinous neoplasms (IPMNs) and compare IPMN growth, neoplastic progression rate, and the value of growth as predictor for neoplastic progression to these in sporadic IPMNs. METHODS: We performed annual surveillance of Dutch HRIs, involving carriers of germline pathogenic variants (PVs) and PV-negative familial pancreatic cancer kindreds. HRIs with IPMNs were compared with Italian individuals without familial risk under surveillance for sporadic IPMNs. RESULTS: A total of 457 HRIs were followed for 48 (range 2-172) months; the estimated cumulative IPMN incidence was 46% (95% confidence interval, 28%-64%). In comparison with 442 control individuals, IPMNs in HRIs were more likely to grow ≥2.5 mm/y (31% vs 7%; P < .001) and develop worrisome features (32% vs 19%; P = .010). PV carriers with IPMNs more often displayed neoplastic progression (n = 3 [11%] vs n = 6 [1%]; P = .011), while familial pancreatic cancer kindreds did not (n = 0 [0%]; P = 1.000). The malignancy risk in a PV carrier with an IPMN was 23% for growth rates ≥2.5 mm/y (n = 13), 30% for ≥5 mm/y (n = 10), and 60% for ≥10 mm/y (n = 5). CONCLUSIONS: The cumulative incidence of IPMNs in HRIs is higher than previously reported in the general population. Compared with sporadic IPMNs, they have an increased growth rate. PV carriers with IPMNs are suggested to be at a higher malignancy risk. Intensive follow-up should be considered for PV carriers with an IPMN growing ≥2.5 mm/y, and surgical resection for those growing ≥5 mm/y.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".