Investigating the effects of a temperature dependent photodynamic dose: A numerical study
Bibliographic record
Abstract
SIGNIFICANCE: Photodynamic therapy (PDT) and photothermal therapy (PTT) show promise as cancer treatments, but challenges in generating large ablative volumes for deep-seated tumours persist. Using simulations, this study investigates combined PDT and PTT to increase treatment volumes, including the impact of a temperature-dependent PDT dose on the treatment volume radius. APPROACH: A finite-element model, using the open-source SfePy package, was developed to simulate combined interstitial photothermal and photodynamic treatments. Results compared an additive dose model to a temperature-dependent dose model with enhanced PDT dosimetry and examined typical clinical scenarios for possible synergistic effects. RESULTS: Findings revealed that the temperature-dependent dose model could significantly expand the damage radius compared to the additive model, depending on the tissue and drug properties. CONCLUSIONS: Characterizing synergistic effects of PDT and PTT could enhance treatment planning. Future work is ongoing to implement additional variables, such as photosensitizer photobleaching, and spatial and temporally varying oxygenation.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".