Role of Neoadjuvant Therapy in the Management of Pancreatic Cancer: Is the Era of Biomarker-Directed Therapy Here?
Bibliographic record
Abstract
Pancreatic cancer is the 4th leading cause of cancer-related death. Complete surgical resection (CR0) is considered the only curative treatment. Most patients present with unresectable or borderline resectable disease. Many small phase i/ii trials have tried to address the role of neoadjuvant treatment using chemotherapy with or without chemoradiation in the management of locally advanced disease. However, many of them looked at the rate of CR0 resection and the feasibility of such treatment. A trend for improved overall survival has been observed in the group of patients with borderline resectable disease who completed neoadjuvant treatment. A large proportion of patients progress while on treatment, sparing them from unnecessary surgery. We searched the PubMed database (using the key words "pancreatic cancer," or "pancreatic neoplasm," or "pancreatic adenocarcinoma," and "neoadjuvant treatment," or "neoadjuvant chemotherapy," or "neoadjuvant radiation therapy," or "neoadjuvant chemo-radiation," or "adjuvant therapy" [all fields] and "clinical trial" or "study") and abstracts presented at the American Society of Clinical Oncology meetings on gastrointestinal cancers. Here, we review the most recent papers that present results on neoadjuvant therapy in pancreatic cancer. All but one report used overall survival as an endpoint. Unfortunately, there are no valid biomarkers predicting tumour progression or recurrence, and response to treatment than can help to guide therapeutic choices. Our recommendation is to consider neoadjuvant treatment in cases of borderline resectable disease. In patients with primary resectable tumours, surgery followed by adjuvant treatment and enrollment on adjuvant treatment studies would be appropriate.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".