A Review of the Dermatologic Clinical Applications of Topical Photodynamic Therapy
Bibliographic record
Abstract
Topical photodynamic therapy is a widely approved therapy for actinic keratoses and low-risk nonmelanoma skin cancers with a rapidly growing range of emerging indications for other cutaneous diseases. This review summarizes the best-available evidence to provide a clinical update for dermatologists on the approved and emerging indications of photodynamic therapy. The body of evidence suggests that photodynamic therapy is superior or noninferior to other available treatment modalities for actinic keratoses, low-risk basal cell carcinomas, Bowen's disease, skin field cancerization, chemoprevention of keratinocyte carcinomas in organ transplant recipients, photoaging, acne vulgaris, and cutaneous infections including verrucae, onychomycosis, and cutaneous leishmaniasis. There is emerging evidence that photodynamic therapy plays a role in the management of actinic cheilitis, early-stage mycosis fungoides, extramammary Paget disease, lichen sclerosis, and folliculitis decalvans but there are no comparative studies with other active treatment modalities. Common barriers to topical photodynamic therapy include procedural pain, costs, and the time required for treatment delivery. There is significant heterogeneity in the photodynamic therapy protocols reported in the literature, including different photosensitizers, light sources, number of treatments, time between treatments, and use of procedural analgesia. Topical photodynamic therapy should be considered in the management of a spectrum of inflammatory, neoplastic, and infectious dermatoses. However, more comparative research is required to determine its role in the treatment algorithm for these dermatologic conditions and more methodological research is required to optimize photodynamic therapy protocols to improve the tolerability of the procedure for patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".