Ablative vs. Non-Ablative Radiotherapy in Palliating Locally Advanced Pancreatic Cancer: A Single Institution Experience and a Systematic Review of the Literature
Bibliographic record
Abstract
We studied the dose–local control (LC) relationship in ablative vs. non-ablative radiotherapy in a non-radical treatment setting of “locally advanced pancreatic cancer (LAPC)” by comparing our patients (n = 89) treated with SBRT on the CyberKnife unit vs. conventional radiation between January 2005 and January 2021, and by reviewing the literature. A systematic search was performed leveraging Medline for references on SBRT use in pancreatic cancer without date terms or language restrictions. A total of 3702 references were identified and the search was then repeated in Embase and the Cochrane database. Ultimately, 12 studies were eligible for inclusion, which either compared SBRT to conventional radiation, or SBRT use in dose escalation for primary LAPC in a non-neoadjuvant setting. Our cohort’s median overall survival was 152 days (CI 95%, 118–185); including 371 days (CI 95%, 230–511) vs. 126 days (CI 95%, 90–161) favoring SBRT, p = 0.004. The median time to local progression was 170 days (48–923) for SBRT vs. 107 days (27–489) for the non-ablative group. In our SBRT patients, no local progressions were seen with BED10 > 60 Gy. Even when palliating LAPC, SBRT should be considered as an alternative to conventional radiation, especially in patients with a low disease burden. BED10 ≥ 60–70 Gy offers better local control without increasing toxicity rates. Less local progression may provide a better quality of life to those patients who already have a short life expectancy.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".