Neoadjuvant Treatment in Localized Pancreatic Cancer of the Elderly: A Systematic Review of the Current Literature
Bibliographic record
Abstract
Background/Objectives: Neoadjuvant therapy (NAT) improves surgical outcomes in pancreatic cancer, but its role in elderly patients remains unclear. Due to comorbidities and lower chemotherapy tolerance, assessing NAT’s benefits and risks in this population is essential. This systematic review assesses the impact of NAT on overall survival (OS), surgical resection rates, and treatment-related toxicities(G3-4) in elderly patients with resectable, borderline, or locally advanced pancreatic cancer. Methods: A systematic search was conducted in PubMed, MEDLINE, EMBASE, Scopus, and Cochrane databases according to PRISMA guidelines. Studies reporting that NAT outcomes in elderly patients (≥70 years) were included. The Newcastle–Ottawa scale was used to assess study quality. Subgroup analyses compared NAT versus upfront surgery and outcomes in elderly versus younger patients. Results: Twelve studies (four prospective and eight retrospective) including 11,385 patients met the inclusion criteria. Among 9580 elderly patients, only 24% underwent NAT. NAT significantly improved R0 resection rates compared to upfront surgery (p < 0.001), and elderly patients receiving NAT had a median OS of 26.5 (range 15.7–39.1) months versus 20.3 months (range 11.5–23.8) of upfront surgery and versus 36.2 months (range 23.6–43.0) of NAT in young patients. Elderly patients experienced higher rates of major toxicities (17–57.5%). Personalized regimens, such as gemcitabine/nab-paclitaxel, were better tolerated than FOLFIRINOX. Conclusions: NAT is associated with improved survival and surgical outcomes in elderly pancreatic cancer patients, despite a higher risk of adverse events. Patient selection based on performance status rather than age alone is essential to optimize treatment benefits. Further prospective trials are needed to refine treatment approaches in this population.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".