Photodynamic Therapy of Multiple Nonmelanoma Skin Cancers With Verteporfinand Red Light–Emitting Diodes
Bibliographic record
Abstract
BACKGROUND: Efficient treatment of patients with multiple synchronous nonmelanoma skin cancers represents a therapeutic challenge. OBJECTIVE: To study the safety and efficacy of photodynamic therapy (PDT) with verteporfin and red light in the treatment of multiple nonmelanoma skin cancers. DESIGN: Open-label, randomized, multicenter, dose-ranging phase 2 study conducted at 4 North American university-based dermatology clinics. PATIENTS: Fifty-four patients with 421 multiple nonmelanoma skin cancers including superficial and nodular basal cell carcinoma and squamous cell carcinoma in situ (Bowen disease). METHODS: A single intravenous infusion of 14 mg/m(2) of verteporfin followed 1 to 3 hours later by exposure of tumors to 60, 120, or 180 J/cm(2) of red light (688 +/- 10 nm) from a light-emitting diode panel. MAIN OUTCOME MEASURES: Pathologic response of treated sites was assessed at 6 months. Clinical and cosmetic responses were assessed and graded at 6 weeks, 3 months, and 6 months after verteporfin PDT, with optional follow-up visits at 12, 18, and 24 months. RESULTS: The histopathologic response, defined as absence of tumor on biopsy specimens 6 months after verteporfin PDT, ranged from 69% at 60 J/cm(2) to 93% at 180 J/cm(2). At 24 months of follow-up (276 tumors in 31 patients), the clinical complete response rate ranged from 51% at 60 J/cm(2) to 95% at 180 J/cm(2). No significant systemic adverse events were observed; most events occurred at the treated tumor sites and included events such as pain. Overall, 65% (95% confidence interval, 58%-71%) of tumors were judged to have good to excellent cosmesis at 24 months. CONCLUSION: A single course of verteporfin PDT showed treatment benefit for patients with multiple nonmelanoma skin cancers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".