Role of neoadjuvant therapy (NAT) in pancreatic adenocarcinoma (PAC): A single center experience.
Bibliographic record
Abstract
e15747 Background: Surgical resection of PAC followed by adjuvant therapy is the standard of care for non-metastatic resectable tumors. Surgical resection with clear margins of borderline (BR) or locally advanced (LA) tumors is either challenging or impossible. Furthermore, there are no clear recommendations concerning NAT for non-upfront resectable PAC. Thus we reviewed our own experience with different NAT modalities for BR and LA PAC. Methods: Medical records of patients identified by Tumor Board as BR or LA PAC and treated with NAT at Centre Hospitalier de l’Université de Montréal (CHUM) were retrospectively reviewed. Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test. For both univariate and subgroup analyses, hazard ratio and 95% confidence interval were estimated by Cox proportional hazard regression. Results: Between 2009 and 2017, 90 patients (50 BR, 40 LA) were identified. Chemotherapy, mostly FOLFIRINOX, was the only NAT in 51 patients (56.6%), 23 patients (25.3%) received chemoradiotherapy alone and 16 patients (17.7%) received sequential treatment of both modalities. Tumor resection was achieved in 44 patients, with 32 BR patients (R0: 68.7%) and 12 LA patients (R0: 75%). Median Disease free survival (DFS) of patients that underwent resection was 12.3 months. mPFS was 29 vs 10 months (HR:0.2; p < 0.001) and mOS was 41.7 vs 15.7 months (HR:0.3; p < 0.001) in resected and non-resected patients, respectively. In subgroup analysis, resection statistically improved PFS and OS regardless of age, sex, T stage and type of vessel involvement. Treatment with more than one modality showed better clinical outcomes (PFS and OS) and a non-statistically higher R0 resection rate that was 100% in BR tumors. OS in patients with resected cancers was not reached for the multimodality group, 41.7 months for chemotherapy alone group and 22.4 months in patients treated with chemoradiotherapy group (p = 0.017). Conclusions: In this retrospective single center analysis, NAT with chemotherapy and/or chemoradiotherapy appears to improve patients’ clinical resection results and outcomes. These results validate previous retrospective studies but warrant large prospective trials to define the best NAT.
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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 | 0.000 | 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".