Use of Protoporphyrin Fluorescence to Determine Clinical Target Volume for Non-melanotic Skin Cancers Treated with Primary Radiotherapy
Bibliographic record
Abstract
PURPOSE: Non-melanotic skin cancers remain the most commonly diagnosed cancers. Radiotherapy and surgery are the most common treatment options. Radiotherapy has a recurrence rate of up to 20% for basal or squamous cell cancers. One of the difficulties is to determine the extent of disease for poorly demarcated tumors. This study utilizes protoporphyrin (PpIX) fluorescence to provide information on the extent of subclinical disease for poorly demarcated tumors treated with radiotherapy. MATERIALS AND METHODS: For 33 patients, PpIX photo-delineation was used to determine the clinical target volume (CTV2), which was compared to current conventional margins used to account for microscopic disease. RESULTS: The use of PpIX photo-delineation demonstrated a significantly larger CTV of 15 mm compared to the conventional 10 mm (p = 0.03) for poorly demarcated lesions. A larger CTV was also demonstrated with PpIX photo-delineation for all basal cell carcinomas (13 mm, p = 0.03) as well as for non-nasal lesions (14 mm, p = 0.04). A trend towards an increased CTV was also noted for squamous cell carcinomas (16 mm, p = 0.19) and nasal primary sites (14 mm, p = 0.11). Nasal primary malignancies had multifocal PpIX uptake in 94% of cases. There was one case of local recurrence and one case of distant recurrence, with an average follow-up time of 22 months. CONCLUSIONS: The margins currently used to account for subclinical disease may underestimate the extent of microscopic spread for poorly demarcated tumors. Longer follow-up with larger pools of patients are necessary to determine if using PpIX photo-delineation translates into significantly improved clinical outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".