Borderline Resectable Pancreatic Cancer: Challenges for Clinical Management
Bibliographic record
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) remains a significant worldwide health problem with a poor prognosis. A borderline resectable pancreatic ductal adenocarcinoma (BR-PDAC) is a tumor with limited vascular involvement that is technically resectable but with a high risk of positive margins (R1 resection). Objective: To identify the current challenges that exist in the management of BR-PDAC. Methods: A review of the literature was conducted to identify articles discussing the definitions and management of BR-PDAC. Key Findings: Several anatomic definitions of BR-PDAC exist, and there is significant heterogeneity in their utilization across published trials. To improve the odds of a margin negative (R0) resection, a neoadjuvant treatment approach involving chemotherapy with or without radiation is currently preferred. While supporting the efficacy of a neoadjuvant approach in BR-PDAC, the largest published randomized trials have utilized older gemcitabine-based regimens. Recently published Phase II evidence and meta-analyses have supported the use of modern multi-agent regimens such as FOLFIRINOX. The utility of adding radiation to a chemotherapy backbone remains in question. Due to remnant fibrosis and edema following neoadjuvant therapy, accurately selecting patients for resection based on a restaging CT scan is challenging. Furthermore, the role of adjuvant therapy in BR-PDAC patients receiving neoadjuvant therapy needs to be defined. Conclusion: Though progress has been made, the optimal management of BR-PDAC is uncertain. Phase III trials utilizing modern chemotherapeutic regimens are needed to establish a standard of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".