Using photodynamic therapy as a neoadjuvant treatment in the surgical excision of nonmelanotic skin cancers: prospective study.
Bibliographic record
Abstract
BACKGROUND: Topical photodynamic therapy (PDT) is a successful treatment for nonmelanotic skin cancers (NMSCs). Nevertheless, surgical excision continues to be the gold standard treatment. Cervicofacial excision often results in functional and aesthetic impairment. We hypothesize that topical PDT as a neoadjuvant therapy to surgical excision may reduce tumour size and subsequently decrease local morbidity. OBJECTIVE: To determine the utility of PDT in reducing the NMSC area for the purpose of surgical excision. METHOD: A prospective cohort study. RESULTS: Thirty-three basal cell carcinomas with a mean area of 523.11 ± 120.93 mm² and 26 squamous cell carcinomas with a mean area of 357.53 ± 61.96 mm² were included. Of these lesions, 22 demonstrated a complete curative response after an average of two PDT treatments, which were then confirmed with histologically negative biopsies. The remaining lesions demonstrated a partial response to topical PDT with a maximum reduction in lesion area following an average of three PDT treatments of at least 88% (p < .05). These lesions were then excised with clear histologic margins. Follow-up at 1 year for all lesions demonstrated no locoregional recurrence. CONCLUSIONS: This is the first study to investigate the efficacy of neoadjuvant topical PDT in the management of NMSC. The results suggest that for NMSC not demonstrating a complete curative response to PDT, neoadjuvant PDT can substantially reduce tumour burden, allowing for less morbid surgical excisions with histologically clear margins.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".