TTP6.08 CA19-9 as an Underutilised Surveillance Tool for IPMN Patients
Bibliographic record
Abstract
Abstract Aims Intraductal papillary mucinous neoplasms (IPMNs) are pre-cancerous pancreatic cystic lesions responsible for up to 20% of pancreatic ductal adenocarcinoma (PDAC) cases due to their malignant potential. Surveillance typically involves imaging with CT or endoscopic ultrasound, both of which are invasive and costly. Current guidelines differ widely, providing limited guidance on the use of CA19-9, a tumour marker that may help assess malignancy risk. This study evaluates the utility of CA19-9 in identifying PDAC risk among IPMN patients, given their risk for both malignant transformation and de novo PDAC. Methods A cohort of 746 IPMN patients was analysed for preoperative CA19-9 levels, resection status, and pathology findings. Patients were grouped into resection (n=27) and non-resection (NR, n=719) cohorts. CA19-9 levels were compared with outcomes. Results Among resected patients, 29.63% had preoperative CA19-9 measurements, while 6.40% of NR patients had at least one CA19-9 measurement. In the NR group, elevated CA19-9 (>37 U/mL) was associated with a higher risk of PDAC (p = 0.0004). Of a total of six cancers in the NR cohort, five were de-novo PDAC and one was malignant transformation of IPMN. Of these, one case involved serial CA19-9 monitoring prior to PDAC confirmation on CT, three had a CA19-9 measurement following PDAC confirmation on CT, and two did not have any form of CA19-9 measured. Conclusion CA19-9 is underutilised in IPMN surveillance despite its potential for identifying high-risk patients. This study underscores the need to refine guidelines to incorporate CA19-9 monitoring for early detection and intervention.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".