Prognostic Significance of Post-Neoadjuvant Chemotherapy Carbohydrate Antigen 19-9 Levels in Patients With Resectable Pancreatic Cancer Treated With S-1 and Gemcitabine: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Carbohydrate antigen 19-9 (CA19-9) is widely used to assess treatment response and monitor recurrence alongside imaging. However, the criteria for determining resectability after completion of neoadjuvant therapy (NAT) remain poorly defined. Therefore, this study aimed to investigate the indications for surgical resection as a prognostic factor following NAT with gemcitabine and S-1 (NATGS). Methods: In this retrospective cohort study, we examined patients who underwent curative pancreatic resection following NATGS at our institution between April 2018 and December 2023. After excluding six patients who did not undergo pancreatectomy, the remaining 50 patients were included in the study. Univariate and multivariate analyses were conducted to identify factors potentially associated with survival after NATGS. Results: Post-NATGS CA19-9 levels (< 100 U/mL) were identified as a significant prognostic factor for disease-free survival (DFS) in both univariate and multivariate analyses (hazard ratio (HR) = 11.72251, P < 0.001). For overall survival (OS), both CA19-9 levels (< 100 U/mL) and Duke pancreatic monoclonal antigen type 2 (DUPAN-2) levels (< 150 U/mL) were significant prognostic factors in univariate and multivariate analyses (CA19-9: HR = 17.88, P = 0.002; DUPAN-2: HR = 2.667, P = 0.03). The median DFS was 24.1 months in the low CA19-9 group compared with the 7.1 months in the high CA19-9 group (P = 0.002). The median OS in the low CA19-9 group was not reached, whereas it was 14.7 months in the high CA19-9 group (P = 0.001). Conclusions: The CA19-9 cut-off value is clinically significant for patients undergoing NATGS regimens. Patients with pre-operative CA19-9 levels ≥ 100 U/mL may benefit from extended GS treatment or a switch to a more potent regimen rather than proceeding directly to surgical resection.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".