Photodynamic therapy dosimetry: current status and the emerging challenge of immune stimulation
Bibliographic record
Abstract
Significance: Addressing the challenges of accurate dosimetry in photodynamic therapy has motivated some of the earliest work in tissue optics, which then enabled the broader development of biomedical optics. Inadequate use of dosimetry-informed treatments may contribute to heterogeneity in tumor response and variable clinical outcomes that need to be addressed. Aim: This perspective paper seeks to understand the current status of photodynamic therapy (PDT) dosimetry in preclinical and clinical applications and identify opportunities for improvement. We also identify the "elephant in the room" of photodynamic immune stimulation that presents additional dosimetry challenges and opportunities. Approach: ) research, PDT dosimetry is often necessary, occasionally used, sometimes effective, and rarely sufficient. The rapid emergence of research on PDT immune stimulation poses existential challenges for PDT dosimetry as practiced to date, which is based on purely biophysical considerations, and possible approaches are suggested that incorporate immunological factors. Results: Different clinical situations require different PDT dosimetry approaches, depending on medical complexity and technical dosimetry requirements. Conclusions: This article is not a comprehensive review, but rather intended to recognize past advances and current limitations, and to stimulate discussion of future directions in PDT dosimetry. Inadequate dosimetry may be a potential impediment to PDT adoption and may have contributed to the failure of some previous and ongoing clinical trials.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".