Photodynamic Therapy for Urological Malignancies: Past to Current Approaches
Bibliographic record
Abstract
PURPOSE: Modern PDT for urological tumors is a potentially selective approach in which in situ photosensitization by a nontoxic drug, locally activated by light, generates cytotoxic reactive oxygen species, causing cell death. While urological clinical experience with PDT is largely limited to treatment for superficial bladder cancer, the advent of novel photosensitizers and technologies for treatment planning, light delivery and dosimetry, PDT for prostate and other urological cancers appears increasingly realistic. MATERIALS AND METHODS: We reviewed the current literature on PDT for urological tumors, in addition to recent emerging data from our laboratory and elsewhere. RESULTS: Remarkable progress has been made in the field of photochemistry and photobiology. Together with improved optical delivery and imaging systems PDT holds promise as an alternative, minimally invasive and potentially curative treatment for localized solid tumors as well as for palliative treatment for isolated, clinically problematic metastases. CONCLUSIONS: Current experience with photodynamic therapy using contemporary photosensitizing agents and light sources is mainly restricted to in vivo experimental models and early phase clinical trails. However, ongoing preclinical work and clinical trials indicate that safer and effective PDT treatments in uro-oncology are imminent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".