Is Biannual Surveillance for Pancreatic Cancer Sufficient in Individuals With Genetic Syndromes or Familial Pancreatic Cancer?
Bibliographic record
Abstract
BACKGROUND: Individuals with a family history of pancreatic adenocarcinoma (PC) or with a germline mutation in a PC susceptibility gene are at increased risk of developing PC. These high-risk individuals (HRIs) may benefit from PC surveillance. METHODS: A PC surveillance program was developed to evaluate the detection of premalignant lesions and early-stage PCs using biannual imaging and to determine whether locally advanced or metastatic PCs develop despite biannual surveillance. From January 2013 to April 2020, asymptomatic HRIs were enrolled and followed with alternating MRI and endoscopic ultrasound every 6 months. RESULTS: Of 75 HRIs, 43 (57.3%) had a germline mutation in a PC susceptibility gene and 32 (42.7%) had a familial pancreatic cancer (FPC) pedigree. Branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) were identified in 26 individuals (34.7%), but only 2 developed progressive lesions. One patient with Peutz-Jeghers syndrome (PJS) developed locally advanced PC arising from a BD-IPMN. Whole-genome sequencing of this patient's PC and of a second patient with PJS-associated PC from the same kindred revealed biallelic inactivation of STK11 in a KRAS-independent manner. A review of 3,853 patients from 2 PC registries identified an additional patient with PJS-associated PC. All 3 patients with PJS developed advanced PC consistent with the malignant transformation of an underlying BD-IPMN in <6 months. The other surveillance patient with a progressive lesion had FPC and underwent resection of a mixed-type IPMN that harbored polyclonal KRAS mutations. CONCLUSIONS: PC surveillance identifies a high prevalence of BD-IPMNs in HRIs. Patients with PJS with BD-IPMNs may be at risk for accelerated malignant transformation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".