Exploring Endovascular Photo-Activated Ablation (EPA) for Downstaging Locally Advanced Pancreatic Cancer: A Proof-of-Concept Study in the Normal Porcine Model
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancers can involve large blood vessels early, making complete resection technically challenging or impossible. A minimally invasive treatment that clears vessels from encasing tumours could potentially enable curative surgery. We hypothesise that Endovascular Photo-activated Ablation (EPA) of perivascular tumour tissue can create a necrotic zone free of viable tumour between cancer and blood vessels, through which the tumour could be resected. METHODS: = 7). Under general anaesthesia, the animals were given a photo-activated drug and photo-activation was provided by a prototype balloon catheter, positioned in a major blood vessel within the pancreas, under angiographic guidance. Contrast-enhanced CT scans were undertaken prior to and 1, 2, or 7 days following ablation. The animals were euthanised and the exposed tissue excised en bloc for histological examination. RESULTS: Five animals were euthanised after 2 days. On post-mortem, the histology confirmed necrotic pancreas in the perivascular zone, which increased from zero to 15 mm around the treated vessel, for increasing drug doses. Treated arteries showed necrotising arteritis, without evidence of perforation or obstruction during the observation period, although one animal was euthanised at 1 day, due to technical endovascular device issues and obstruction. The lowest-dose animal euthanised at 7 days showed no lesions on pathology. CONCLUSIONS: These proof-of-concept results demonstrate that EPA can produce pancreatic perivascular necrosis in a large animal model. In a pancreatic cancer abutting a major blood vessel, this procedure may be able to create a zone free of viable tumour, potentially rendering these cancers operable, while preserving vessel integrity. These findings support further research activities towards clinical translation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".